#%matplotlib inline
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML

# Set random seed for reproducibility
manualSeed = 999
#manualSeed = random.randint(1, 10000) # use if you want new results
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
torch.use_deterministic_algorithms(True) # Needed for reproducible results
from pathlib import Path

Random Seed:  999

# Root directory for dataset
dataroot = Path("../data/leedsbutterfly/images")

# Number of workers for dataloader
workers = 2

# Batch size during training
batch_size = 256

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64

# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 100

# Size of feature maps in generator
ngf = 64

# Size of feature maps in discriminator
ndf = 64

# Number of training epochs
num_epochs = 1000

# Learning rate for optimizers
lr = 0.0002

# Beta1 hyperparameter for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1

# We can use an image folder dataset the way we have it setup.
# Create the dataset
dataset = dset.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                           ]))
# Create the dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=workers)

# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")

# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8,8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))

<matplotlib.image.AxesImage at 0x7f606154ed60>

# custom weights initialization called on ``netG`` and ``netD``
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. ``(ngf*8) x 4 x 4``
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. ``(ngf*4) x 8 x 8``
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. ``(ngf*2) x 16 x 16``
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. ``(ngf) x 32 x 32``
            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. ``(nc) x 64 x 64``
        )

    def forward(self, input):
        return self.main(input)

# Create the generator
netG = Generator(ngpu).to(device)

# Handle multi-GPU if desired
if (device.type == 'cuda') and (ngpu > 1):
    netG = nn.DataParallel(netG, list(range(ngpu)))

# Apply the ``weights_init`` function to randomly initialize all weights
#  to ``mean=0``, ``stdev=0.02``.
netG.apply(weights_init)

# Print the model
print(netG)

Generator(
  (main): Sequential(
    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace=True)
    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace=True)
    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (11): ReLU(inplace=True)
    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is ``(nc) x 64 x 64``
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. ``(ndf) x 32 x 32``
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. ``(ndf*2) x 16 x 16``
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. ``(ndf*4) x 8 x 8``
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. ``(ndf*8) x 4 x 4``
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)

# Create the Discriminator
netD = Discriminator(ngpu).to(device)

# Handle multi-GPU if desired
if (device.type == 'cuda') and (ngpu > 1):
    netD = nn.DataParallel(netD, list(range(ngpu)))

# Apply the ``weights_init`` function to randomly initialize all weights
# like this: ``to mean=0, stdev=0.2``.
netD.apply(weights_init)

# Print the model
print(netD)

Discriminator(
  (main): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (4): LeakyReLU(negative_slope=0.2, inplace=True)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (10): LeakyReLU(negative_slope=0.2, inplace=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (12): Sigmoid()
  )
)

# Initialize the ``BCELoss`` function
criterion = nn.BCELoss()

# Create batch of latent vectors that we will use to visualize
#  the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)

# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.

# Setup Adam optimizers for both G and D
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

# Training Loop

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
    # For each batch in the dataloader
    for i, data in enumerate(dataloader, 0):

        ############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################
        ## Train with all-real batch
        netD.zero_grad()
        # Format batch
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
        # Forward pass real batch through D
        output = netD(real_cpu).view(-1)
        # Calculate loss on all-real batch
        errD_real = criterion(output, label)
        # Calculate gradients for D in backward pass
        errD_real.backward()
        D_x = output.mean().item()

        ## Train with all-fake batch
        # Generate batch of latent vectors
        noise = torch.randn(b_size, nz, 1, 1, device=device)
        # Generate fake image batch with G
        fake = netG(noise)
        label.fill_(fake_label)
        # Classify all fake batch with D
        output = netD(fake.detach()).view(-1)
        # Calculate D's loss on the all-fake batch
        errD_fake = criterion(output, label)
        # Calculate the gradients for this batch, accumulated (summed) with previous gradients
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        # Compute error of D as sum over the fake and the real batches
        errD = errD_real + errD_fake
        # Update D
        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        netG.zero_grad()
        label.fill_(real_label)  # fake labels are real for generator cost
        # Since we just updated D, perform another forward pass of all-fake batch through D
        output = netD(fake).view(-1)
        # Calculate G's loss based on this output
        errG = criterion(output, label)
        # Calculate gradients for G
        errG.backward()
        D_G_z2 = output.mean().item()
        # Update G
        optimizerG.step()

        # Output training stats
        if i % 50 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        # Save Losses for plotting later
        G_losses.append(errG.item())
        D_losses.append(errD.item())

        # Check how the generator is doing by saving G's output on fixed_noise
        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1

Starting Training Loop...
[0/1000][0/4]	Loss_D: 1.5046	Loss_G: 6.4261	D(x): 0.7725	D(G(z)): 0.6382 / 0.0030
[1/1000][0/4]	Loss_D: 0.1413	Loss_G: 7.3645	D(x): 0.9813	D(G(z)): 0.1048 / 0.0010
[2/1000][0/4]	Loss_D: 0.1555	Loss_G: 9.3220	D(x): 0.9699	D(G(z)): 0.0988 / 0.0002
[3/1000][0/4]	Loss_D: 0.0768	Loss_G: 10.3860	D(x): 0.9923	D(G(z)): 0.0644 / 0.0001
[4/1000][0/4]	Loss_D: 0.0289	Loss_G: 7.1842	D(x): 0.9969	D(G(z)): 0.0251 / 0.0010
[5/1000][0/4]	Loss_D: 0.0113	Loss_G: 11.4426	D(x): 0.9904	D(G(z)): 0.0000 / 0.0000
[6/1000][0/4]	Loss_D: 0.2582	Loss_G: 13.6945	D(x): 0.9807	D(G(z)): 0.1919 / 0.0000
[7/1000][0/4]	Loss_D: 0.0041	Loss_G: 9.0673	D(x): 0.9966	D(G(z)): 0.0006 / 0.0002
[8/1000][0/4]	Loss_D: 0.0326	Loss_G: 15.9179	D(x): 0.9759	D(G(z)): 0.0000 / 0.0000
[9/1000][0/4]	Loss_D: 0.0283	Loss_G: 23.9272	D(x): 0.9816	D(G(z)): 0.0000 / 0.0000
[10/1000][0/4]	Loss_D: 4.2341	Loss_G: 24.3291	D(x): 0.1203	D(G(z)): 0.0000 / 0.0000
[11/1000][0/4]	Loss_D: 1.3951	Loss_G: 24.2137	D(x): 0.5884	D(G(z)): 0.0000 / 0.0000
[12/1000][0/4]	Loss_D: 0.2100	Loss_G: 11.7406	D(x): 0.9052	D(G(z)): 0.0043 / 0.0000
[13/1000][0/4]	Loss_D: 0.0917	Loss_G: 5.3276	D(x): 0.9585	D(G(z)): 0.0026 / 0.0236
[14/1000][0/4]	Loss_D: 0.3755	Loss_G: 8.7706	D(x): 0.9481	D(G(z)): 0.2118 / 0.0003
[15/1000][0/4]	Loss_D: 2.9367	Loss_G: 10.9268	D(x): 0.1400	D(G(z)): 0.0000 / 0.0001
[16/1000][0/4]	Loss_D: 0.1446	Loss_G: 3.3003	D(x): 0.9538	D(G(z)): 0.0693 / 0.0970
[17/1000][0/4]	Loss_D: 1.4716	Loss_G: 9.4209	D(x): 0.9440	D(G(z)): 0.6716 / 0.0003
[18/1000][0/4]	Loss_D: 0.7138	Loss_G: 4.4008	D(x): 0.6086	D(G(z)): 0.0265 / 0.0261
[19/1000][0/4]	Loss_D: 0.6162	Loss_G: 2.8658	D(x): 0.6927	D(G(z)): 0.1197 / 0.0973
[20/1000][0/4]	Loss_D: 0.3841	Loss_G: 7.0203	D(x): 0.7599	D(G(z)): 0.0243 / 0.0037
[21/1000][0/4]	Loss_D: 0.5286	Loss_G: 3.0276	D(x): 0.7085	D(G(z)): 0.0764 / 0.0858
[22/1000][0/4]	Loss_D: 0.3971	Loss_G: 4.1386	D(x): 0.8549	D(G(z)): 0.1716 / 0.0264
[23/1000][0/4]	Loss_D: 0.4082	Loss_G: 4.6194	D(x): 0.8903	D(G(z)): 0.2205 / 0.0154
[24/1000][0/4]	Loss_D: 0.2565	Loss_G: 2.6626	D(x): 0.8599	D(G(z)): 0.0766 / 0.0931
[25/1000][0/4]	Loss_D: 0.2333	Loss_G: 4.6640	D(x): 0.8439	D(G(z)): 0.0338 / 0.0150
[26/1000][0/4]	Loss_D: 0.1857	Loss_G: 3.8834	D(x): 0.8953	D(G(z)): 0.0548 / 0.0318
[27/1000][0/4]	Loss_D: 0.2463	Loss_G: 4.2136	D(x): 0.9378	D(G(z)): 0.1550 / 0.0208
[28/1000][0/4]	Loss_D: 0.1853	Loss_G: 4.1592	D(x): 0.9328	D(G(z)): 0.1003 / 0.0223
[29/1000][0/4]	Loss_D: 0.2134	Loss_G: 3.0034	D(x): 0.8633	D(G(z)): 0.0471 / 0.0668
[30/1000][0/4]	Loss_D: 0.2082	Loss_G: 3.5492	D(x): 0.8813	D(G(z)): 0.0682 / 0.0386
[31/1000][0/4]	Loss_D: 0.1957	Loss_G: 4.2483	D(x): 0.9021	D(G(z)): 0.0807 / 0.0175
[32/1000][0/4]	Loss_D: 1.4930	Loss_G: 17.5899	D(x): 0.9943	D(G(z)): 0.7484 / 0.0000
[33/1000][0/4]	Loss_D: 3.7308	Loss_G: 1.6145	D(x): 0.1076	D(G(z)): 0.0035 / 0.3027
[34/1000][0/4]	Loss_D: 0.2621	Loss_G: 3.5601	D(x): 0.9257	D(G(z)): 0.1453 / 0.0568
[35/1000][0/4]	Loss_D: 0.4175	Loss_G: 4.6490	D(x): 0.9320	D(G(z)): 0.2702 / 0.0151
[36/1000][0/4]	Loss_D: 0.1976	Loss_G: 3.8200	D(x): 0.8759	D(G(z)): 0.0503 / 0.0304
[37/1000][0/4]	Loss_D: 0.3084	Loss_G: 4.0939	D(x): 0.9223	D(G(z)): 0.1901 / 0.0236
[38/1000][0/4]	Loss_D: 0.3624	Loss_G: 2.5138	D(x): 0.7545	D(G(z)): 0.0460 / 0.1051
[39/1000][0/4]	Loss_D: 0.2063	Loss_G: 5.6655	D(x): 0.9812	D(G(z)): 0.1620 / 0.0053
[40/1000][0/4]	Loss_D: 0.2904	Loss_G: 3.0831	D(x): 0.8365	D(G(z)): 0.0865 / 0.0605
[41/1000][0/4]	Loss_D: 0.1502	Loss_G: 4.2130	D(x): 0.9367	D(G(z)): 0.0751 / 0.0213
[42/1000][0/4]	Loss_D: 0.1384	Loss_G: 3.6566	D(x): 0.8950	D(G(z)): 0.0209 / 0.0329
[43/1000][0/4]	Loss_D: 0.2466	Loss_G: 5.0576	D(x): 0.9527	D(G(z)): 0.1687 / 0.0091
[44/1000][0/4]	Loss_D: 0.6266	Loss_G: 0.3780	D(x): 0.5871	D(G(z)): 0.0092 / 0.6985
[45/1000][0/4]	Loss_D: 0.4704	Loss_G: 6.5676	D(x): 0.9880	D(G(z)): 0.3252 / 0.0022
[46/1000][0/4]	Loss_D: 0.1044	Loss_G: 2.4833	D(x): 0.9727	D(G(z)): 0.0706 / 0.1194
[47/1000][0/4]	Loss_D: 0.3262	Loss_G: 4.2876	D(x): 0.9607	D(G(z)): 0.2371 / 0.0201
[48/1000][0/4]	Loss_D: 0.2243	Loss_G: 4.5139	D(x): 0.8426	D(G(z)): 0.0327 / 0.0161
[49/1000][0/4]	Loss_D: 0.1831	Loss_G: 5.2333	D(x): 0.9709	D(G(z)): 0.1370 / 0.0075
[50/1000][0/4]	Loss_D: 0.1563	Loss_G: 4.6470	D(x): 0.9424	D(G(z)): 0.0876 / 0.0135
[51/1000][0/4]	Loss_D: 0.3353	Loss_G: 2.0913	D(x): 0.7648	D(G(z)): 0.0300 / 0.1514
[52/1000][0/4]	Loss_D: 0.2612	Loss_G: 7.2471	D(x): 0.9836	D(G(z)): 0.1976 / 0.0023
[53/1000][0/4]	Loss_D: 1.0680	Loss_G: 4.0189	D(x): 0.4691	D(G(z)): 0.0013 / 0.0300
[54/1000][0/4]	Loss_D: 0.2883	Loss_G: 2.9682	D(x): 0.8053	D(G(z)): 0.0366 / 0.0827
[55/1000][0/4]	Loss_D: 0.8484	Loss_G: 4.4595	D(x): 0.5214	D(G(z)): 0.0019 / 0.0235
[56/1000][0/4]	Loss_D: 0.1931	Loss_G: 4.3759	D(x): 0.9704	D(G(z)): 0.1319 / 0.0227
[57/1000][0/4]	Loss_D: 1.6456	Loss_G: 1.1376	D(x): 0.3346	D(G(z)): 0.0032 / 0.3800
[58/1000][0/4]	Loss_D: 0.2730	Loss_G: 4.1542	D(x): 0.8840	D(G(z)): 0.1011 / 0.0342
[59/1000][0/4]	Loss_D: 0.6864	Loss_G: 1.2841	D(x): 0.6123	D(G(z)): 0.0475 / 0.3292
[60/1000][0/4]	Loss_D: 0.3388	Loss_G: 4.3617	D(x): 0.9868	D(G(z)): 0.2475 / 0.0224
[61/1000][0/4]	Loss_D: 0.4623	Loss_G: 2.4965	D(x): 0.7093	D(G(z)): 0.0500 / 0.1215
[62/1000][0/4]	Loss_D: 1.4708	Loss_G: 3.1038	D(x): 0.3316	D(G(z)): 0.0039 / 0.0952
[63/1000][0/4]	Loss_D: 0.3209	Loss_G: 3.4864	D(x): 0.9331	D(G(z)): 0.2019 / 0.0495
[64/1000][0/4]	Loss_D: 0.3638	Loss_G: 2.4843	D(x): 0.7503	D(G(z)): 0.0436 / 0.1128
[65/1000][0/4]	Loss_D: 0.4109	Loss_G: 5.5267	D(x): 0.9673	D(G(z)): 0.2900 / 0.0066
[66/1000][0/4]	Loss_D: 0.2627	Loss_G: 3.0178	D(x): 0.8542	D(G(z)): 0.0789 / 0.0736
[67/1000][0/4]	Loss_D: 0.4442	Loss_G: 2.5456	D(x): 0.7200	D(G(z)): 0.0556 / 0.1279
[68/1000][0/4]	Loss_D: 0.3577	Loss_G: 2.4924	D(x): 0.7679	D(G(z)): 0.0419 / 0.1281
[69/1000][0/4]	Loss_D: 0.9626	Loss_G: 1.2129	D(x): 0.4506	D(G(z)): 0.0073 / 0.3600
[70/1000][0/4]	Loss_D: 4.6596	Loss_G: 0.9570	D(x): 0.0271	D(G(z)): 0.0011 / 0.5211
[71/1000][0/4]	Loss_D: 0.8383	Loss_G: 5.6587	D(x): 0.9561	D(G(z)): 0.4657 / 0.0068
[72/1000][0/4]	Loss_D: 0.3727	Loss_G: 3.2468	D(x): 0.8536	D(G(z)): 0.1646 / 0.0565
[73/1000][0/4]	Loss_D: 0.4541	Loss_G: 4.8338	D(x): 0.9237	D(G(z)): 0.2827 / 0.0146
[74/1000][0/4]	Loss_D: 0.3473	Loss_G: 2.8330	D(x): 0.8334	D(G(z)): 0.1142 / 0.0914
[75/1000][0/4]	Loss_D: 0.3199	Loss_G: 2.9908	D(x): 0.8023	D(G(z)): 0.0714 / 0.0723
[76/1000][0/4]	Loss_D: 0.3228	Loss_G: 3.7281	D(x): 0.8675	D(G(z)): 0.1440 / 0.0349
[77/1000][0/4]	Loss_D: 0.4410	Loss_G: 4.6912	D(x): 0.8766	D(G(z)): 0.2457 / 0.0142
[78/1000][0/4]	Loss_D: 1.3767	Loss_G: 6.8126	D(x): 0.9914	D(G(z)): 0.6764 / 0.0023
[79/1000][0/4]	Loss_D: 0.4919	Loss_G: 2.9949	D(x): 0.6969	D(G(z)): 0.0514 / 0.0838
[80/1000][0/4]	Loss_D: 0.6816	Loss_G: 1.8426	D(x): 0.5685	D(G(z)): 0.0199 / 0.2152
[81/1000][0/4]	Loss_D: 0.2666	Loss_G: 5.0439	D(x): 0.9348	D(G(z)): 0.1658 / 0.0096
[82/1000][0/4]	Loss_D: 1.4857	Loss_G: 8.1452	D(x): 0.9911	D(G(z)): 0.7167 / 0.0007
[83/1000][0/4]	Loss_D: 0.2499	Loss_G: 2.7396	D(x): 0.8640	D(G(z)): 0.0820 / 0.1029
[84/1000][0/4]	Loss_D: 0.5257	Loss_G: 2.8737	D(x): 0.6476	D(G(z)): 0.0267 / 0.0791
[85/1000][0/4]	Loss_D: 0.3957	Loss_G: 3.9161	D(x): 0.9111	D(G(z)): 0.2386 / 0.0284
[86/1000][0/4]	Loss_D: 1.1209	Loss_G: 6.6056	D(x): 0.9868	D(G(z)): 0.6240 / 0.0031
[87/1000][0/4]	Loss_D: 0.2919	Loss_G: 3.0157	D(x): 0.8137	D(G(z)): 0.0628 / 0.0766
[88/1000][0/4]	Loss_D: 0.4273	Loss_G: 3.9696	D(x): 0.9067	D(G(z)): 0.2604 / 0.0269
[89/1000][0/4]	Loss_D: 0.8909	Loss_G: 6.4159	D(x): 0.9796	D(G(z)): 0.5346 / 0.0028
[90/1000][0/4]	Loss_D: 0.3025	Loss_G: 3.0488	D(x): 0.9095	D(G(z)): 0.1688 / 0.0720
[91/1000][0/4]	Loss_D: 0.3899	Loss_G: 2.2378	D(x): 0.7378	D(G(z)): 0.0589 / 0.1434
[92/1000][0/4]	Loss_D: 0.3327	Loss_G: 2.8739	D(x): 0.7663	D(G(z)): 0.0432 / 0.0784
[93/1000][0/4]	Loss_D: 0.4802	Loss_G: 5.1903	D(x): 0.9400	D(G(z)): 0.3092 / 0.0105
[94/1000][0/4]	Loss_D: 0.2770	Loss_G: 2.6110	D(x): 0.8966	D(G(z)): 0.1332 / 0.1102
[95/1000][0/4]	Loss_D: 0.3264	Loss_G: 3.4158	D(x): 0.8870	D(G(z)): 0.1653 / 0.0536
[96/1000][0/4]	Loss_D: 0.2910	Loss_G: 2.2766	D(x): 0.8233	D(G(z)): 0.0787 / 0.1313
[97/1000][0/4]	Loss_D: 2.2393	Loss_G: 1.9751	D(x): 0.1650	D(G(z)): 0.0039 / 0.2044
[98/1000][0/4]	Loss_D: 0.7914	Loss_G: 3.8871	D(x): 0.5681	D(G(z)): 0.0531 / 0.0420
[99/1000][0/4]	Loss_D: 1.5368	Loss_G: 5.1121	D(x): 0.9736	D(G(z)): 0.7366 / 0.0144
[100/1000][0/4]	Loss_D: 0.3957	Loss_G: 2.4391	D(x): 0.7738	D(G(z)): 0.1044 / 0.1141
[101/1000][0/4]	Loss_D: 0.4001	Loss_G: 2.6844	D(x): 0.8291	D(G(z)): 0.1734 / 0.0855
[102/1000][0/4]	Loss_D: 0.5685	Loss_G: 1.3473	D(x): 0.6577	D(G(z)): 0.0936 / 0.2951
[103/1000][0/4]	Loss_D: 0.3209	Loss_G: 3.3052	D(x): 0.8201	D(G(z)): 0.1025 / 0.0481
[104/1000][0/4]	Loss_D: 0.3352	Loss_G: 2.5980	D(x): 0.8537	D(G(z)): 0.1505 / 0.0952
[105/1000][0/4]	Loss_D: 0.4755	Loss_G: 4.6078	D(x): 0.9432	D(G(z)): 0.3177 / 0.0148
[106/1000][0/4]	Loss_D: 0.6790	Loss_G: 4.9194	D(x): 0.9838	D(G(z)): 0.4372 / 0.0113
[107/1000][0/4]	Loss_D: 2.1461	Loss_G: 6.1552	D(x): 0.9973	D(G(z)): 0.8317 / 0.0040
[108/1000][0/4]	Loss_D: 0.6480	Loss_G: 1.9052	D(x): 0.5810	D(G(z)): 0.0329 / 0.1844
[109/1000][0/4]	Loss_D: 0.3370	Loss_G: 3.2436	D(x): 0.9018	D(G(z)): 0.1932 / 0.0531
[110/1000][0/4]	Loss_D: 0.3842	Loss_G: 2.4904	D(x): 0.8135	D(G(z)): 0.1490 / 0.1045
[111/1000][0/4]	Loss_D: 0.3631	Loss_G: 2.5002	D(x): 0.7750	D(G(z)): 0.0851 / 0.1061
[112/1000][0/4]	Loss_D: 0.3537	Loss_G: 3.5618	D(x): 0.8816	D(G(z)): 0.1875 / 0.0408
[113/1000][0/4]	Loss_D: 0.3315	Loss_G: 3.6177	D(x): 0.8998	D(G(z)): 0.1877 / 0.0381
[114/1000][0/4]	Loss_D: 0.3580	Loss_G: 1.8256	D(x): 0.7502	D(G(z)): 0.0478 / 0.1895
[115/1000][0/4]	Loss_D: 2.2498	Loss_G: 1.9888	D(x): 0.1473	D(G(z)): 0.0018 / 0.1921
[116/1000][0/4]	Loss_D: 2.0357	Loss_G: 0.9320	D(x): 0.1808	D(G(z)): 0.0049 / 0.4642
[117/1000][0/4]	Loss_D: 0.7428	Loss_G: 4.7057	D(x): 0.9488	D(G(z)): 0.4648 / 0.0135
[118/1000][0/4]	Loss_D: 0.5284	Loss_G: 4.0327	D(x): 0.9276	D(G(z)): 0.3456 / 0.0245
[119/1000][0/4]	Loss_D: 0.3691	Loss_G: 3.4717	D(x): 0.9038	D(G(z)): 0.2235 / 0.0392
[120/1000][0/4]	Loss_D: 0.4503	Loss_G: 4.2273	D(x): 0.9347	D(G(z)): 0.3025 / 0.0189
[121/1000][0/4]	Loss_D: 0.3135	Loss_G: 2.8060	D(x): 0.8491	D(G(z)): 0.1287 / 0.0760
[122/1000][0/4]	Loss_D: 0.3103	Loss_G: 3.3234	D(x): 0.8494	D(G(z)): 0.1253 / 0.0492
[123/1000][0/4]	Loss_D: 0.6839	Loss_G: 6.3258	D(x): 0.9670	D(G(z)): 0.4566 / 0.0032
[124/1000][0/4]	Loss_D: 0.3738	Loss_G: 3.7806	D(x): 0.9726	D(G(z)): 0.2727 / 0.0331
[125/1000][0/4]	Loss_D: 0.3806	Loss_G: 4.7427	D(x): 0.9381	D(G(z)): 0.2579 / 0.0115
[126/1000][0/4]	Loss_D: 0.2153	Loss_G: 2.6760	D(x): 0.9137	D(G(z)): 0.1111 / 0.0894
[127/1000][0/4]	Loss_D: 0.2406	Loss_G: 3.6697	D(x): 0.9271	D(G(z)): 0.1445 / 0.0349
[128/1000][0/4]	Loss_D: 0.3061	Loss_G: 2.8573	D(x): 0.8525	D(G(z)): 0.1266 / 0.0738
[129/1000][0/4]	Loss_D: 0.3216	Loss_G: 2.3409	D(x): 0.8002	D(G(z)): 0.0828 / 0.1209
[130/1000][0/4]	Loss_D: 0.8374	Loss_G: 1.3866	D(x): 0.4777	D(G(z)): 0.0142 / 0.2950
[131/1000][0/4]	Loss_D: 0.2813	Loss_G: 3.2093	D(x): 0.8484	D(G(z)): 0.0962 / 0.0564
[132/1000][0/4]	Loss_D: 0.3291	Loss_G: 2.9375	D(x): 0.8446	D(G(z)): 0.1363 / 0.0674
[133/1000][0/4]	Loss_D: 0.3512	Loss_G: 3.3843	D(x): 0.8627	D(G(z)): 0.1738 / 0.0423
[134/1000][0/4]	Loss_D: 0.4190	Loss_G: 5.6182	D(x): 0.9611	D(G(z)): 0.2961 / 0.0065
[135/1000][0/4]	Loss_D: 0.4672	Loss_G: 2.6607	D(x): 0.8317	D(G(z)): 0.1964 / 0.0954
[136/1000][0/4]	Loss_D: 1.7680	Loss_G: 1.5962	D(x): 0.2255	D(G(z)): 0.0067 / 0.2674
[137/1000][0/4]	Loss_D: 0.8131	Loss_G: 5.3189	D(x): 0.8467	D(G(z)): 0.4321 / 0.0079
[138/1000][0/4]	Loss_D: 0.3109	Loss_G: 2.1031	D(x): 0.8404	D(G(z)): 0.1142 / 0.1548
[139/1000][0/4]	Loss_D: 0.2722	Loss_G: 4.4246	D(x): 0.8850	D(G(z)): 0.1239 / 0.0174
[140/1000][0/4]	Loss_D: 0.4293	Loss_G: 2.9562	D(x): 0.9002	D(G(z)): 0.2567 / 0.0673
[141/1000][0/4]	Loss_D: 0.5973	Loss_G: 1.7873	D(x): 0.6065	D(G(z)): 0.0489 / 0.2071
[142/1000][0/4]	Loss_D: 0.3424	Loss_G: 3.0054	D(x): 0.8155	D(G(z)): 0.1121 / 0.0662
[143/1000][0/4]	Loss_D: 0.4124	Loss_G: 3.3366	D(x): 0.8827	D(G(z)): 0.2355 / 0.0470
[144/1000][0/4]	Loss_D: 0.8345	Loss_G: 5.5943	D(x): 0.9733	D(G(z)): 0.5133 / 0.0070
[145/1000][0/4]	Loss_D: 0.2829	Loss_G: 2.6988	D(x): 0.8721	D(G(z)): 0.1260 / 0.0849
[146/1000][0/4]	Loss_D: 0.3824	Loss_G: 2.0371	D(x): 0.7799	D(G(z)): 0.1087 / 0.1567
[147/1000][0/4]	Loss_D: 0.7043	Loss_G: 1.4657	D(x): 0.5352	D(G(z)): 0.0171 / 0.2690
[148/1000][0/4]	Loss_D: 0.2576	Loss_G: 3.1980	D(x): 0.8720	D(G(z)): 0.1048 / 0.0590
[149/1000][0/4]	Loss_D: 0.4613	Loss_G: 4.4346	D(x): 0.9056	D(G(z)): 0.2834 / 0.0179
[150/1000][0/4]	Loss_D: 1.2256	Loss_G: 8.4685	D(x): 0.9922	D(G(z)): 0.6455 / 0.0004
[151/1000][0/4]	Loss_D: 0.3647	Loss_G: 1.9762	D(x): 0.7773	D(G(z)): 0.0773 / 0.1842
[152/1000][0/4]	Loss_D: 0.5576	Loss_G: 4.1067	D(x): 0.9018	D(G(z)): 0.3349 / 0.0232
[153/1000][0/4]	Loss_D: 0.5249	Loss_G: 3.4100	D(x): 0.9343	D(G(z)): 0.3430 / 0.0432
[154/1000][0/4]	Loss_D: 0.3824	Loss_G: 2.4822	D(x): 0.8045	D(G(z)): 0.1350 / 0.1014
[155/1000][0/4]	Loss_D: 0.7657	Loss_G: 5.5089	D(x): 0.9566	D(G(z)): 0.4872 / 0.0059
[156/1000][0/4]	Loss_D: 0.3240	Loss_G: 2.2223	D(x): 0.7668	D(G(z)): 0.0386 / 0.1408
[157/1000][0/4]	Loss_D: 0.3788	Loss_G: 3.4786	D(x): 0.9194	D(G(z)): 0.2385 / 0.0404
[158/1000][0/4]	Loss_D: 0.4530	Loss_G: 3.6647	D(x): 0.9035	D(G(z)): 0.2781 / 0.0335
[159/1000][0/4]	Loss_D: 0.3675	Loss_G: 3.8356	D(x): 0.9411	D(G(z)): 0.2482 / 0.0296
[160/1000][0/4]	Loss_D: 0.6028	Loss_G: 5.1621	D(x): 0.9636	D(G(z)): 0.4014 / 0.0094
[161/1000][0/4]	Loss_D: 1.0819	Loss_G: 6.4298	D(x): 0.9895	D(G(z)): 0.6072 / 0.0031
[162/1000][0/4]	Loss_D: 0.3328	Loss_G: 3.6497	D(x): 0.9409	D(G(z)): 0.2247 / 0.0359
[163/1000][0/4]	Loss_D: 0.2893	Loss_G: 3.2135	D(x): 0.8821	D(G(z)): 0.1418 / 0.0553
[164/1000][0/4]	Loss_D: 0.3118	Loss_G: 3.7017	D(x): 0.9090	D(G(z)): 0.1868 / 0.0307
[165/1000][0/4]	Loss_D: 0.6886	Loss_G: 6.6013	D(x): 0.9661	D(G(z)): 0.4504 / 0.0025
[166/1000][0/4]	Loss_D: 0.5941	Loss_G: 6.1662	D(x): 0.9725	D(G(z)): 0.4002 / 0.0032
[167/1000][0/4]	Loss_D: 0.6094	Loss_G: 1.4198	D(x): 0.6286	D(G(z)): 0.0578 / 0.3041
[168/1000][0/4]	Loss_D: 0.6028	Loss_G: 1.9366	D(x): 0.6531	D(G(z)): 0.1217 / 0.1847
[169/1000][0/4]	Loss_D: 1.0126	Loss_G: 1.3864	D(x): 0.4278	D(G(z)): 0.0298 / 0.3053
[170/1000][0/4]	Loss_D: 0.5637	Loss_G: 3.9594	D(x): 0.9479	D(G(z)): 0.3676 / 0.0285
[171/1000][0/4]	Loss_D: 0.4115	Loss_G: 2.6668	D(x): 0.8424	D(G(z)): 0.1972 / 0.0871
[172/1000][0/4]	Loss_D: 0.3529	Loss_G: 2.2878	D(x): 0.8293	D(G(z)): 0.1416 / 0.1217
[173/1000][0/4]	Loss_D: 0.3603	Loss_G: 2.0036	D(x): 0.7743	D(G(z)): 0.0861 / 0.1564
[174/1000][0/4]	Loss_D: 0.3082	Loss_G: 3.2452	D(x): 0.8989	D(G(z)): 0.1737 / 0.0480
[175/1000][0/4]	Loss_D: 0.3428	Loss_G: 3.1809	D(x): 0.9202	D(G(z)): 0.2145 / 0.0564
[176/1000][0/4]	Loss_D: 0.3107	Loss_G: 2.4054	D(x): 0.7875	D(G(z)): 0.0583 / 0.1146
[177/1000][0/4]	Loss_D: 0.2469	Loss_G: 3.1919	D(x): 0.8924	D(G(z)): 0.1179 / 0.0557
[178/1000][0/4]	Loss_D: 0.3579	Loss_G: 1.6311	D(x): 0.7449	D(G(z)): 0.0451 / 0.2295
[179/1000][0/4]	Loss_D: 0.2450	Loss_G: 3.5441	D(x): 0.8760	D(G(z)): 0.0979 / 0.0395
[180/1000][0/4]	Loss_D: 0.2848	Loss_G: 2.4151	D(x): 0.8504	D(G(z)): 0.1052 / 0.1136
[181/1000][0/4]	Loss_D: 0.6642	Loss_G: 0.9362	D(x): 0.5544	D(G(z)): 0.0180 / 0.4401
[182/1000][0/4]	Loss_D: 1.4925	Loss_G: 1.1317	D(x): 0.2933	D(G(z)): 0.0106 / 0.4412
[183/1000][0/4]	Loss_D: 0.5422	Loss_G: 3.9119	D(x): 0.9522	D(G(z)): 0.3553 / 0.0280
[184/1000][0/4]	Loss_D: 0.3635	Loss_G: 3.1620	D(x): 0.8684	D(G(z)): 0.1835 / 0.0565
[185/1000][0/4]	Loss_D: 0.5787	Loss_G: 5.1990	D(x): 0.9513	D(G(z)): 0.3829 / 0.0088
[186/1000][0/4]	Loss_D: 0.2667	Loss_G: 2.7125	D(x): 0.8668	D(G(z)): 0.1080 / 0.0847
[187/1000][0/4]	Loss_D: 0.3628	Loss_G: 4.3377	D(x): 0.9490	D(G(z)): 0.2515 / 0.0181
[188/1000][0/4]	Loss_D: 0.2609	Loss_G: 2.6256	D(x): 0.8531	D(G(z)): 0.0892 / 0.0897
[189/1000][0/4]	Loss_D: 0.3054	Loss_G: 2.8349	D(x): 0.8620	D(G(z)): 0.1352 / 0.0754
[190/1000][0/4]	Loss_D: 0.3032	Loss_G: 2.2329	D(x): 0.7943	D(G(z)): 0.0608 / 0.1390
[191/1000][0/4]	Loss_D: 0.3142	Loss_G: 2.3438	D(x): 0.7809	D(G(z)): 0.0541 / 0.1264
[192/1000][0/4]	Loss_D: 0.2348	Loss_G: 2.9159	D(x): 0.8995	D(G(z)): 0.1148 / 0.0701
[193/1000][0/4]	Loss_D: 0.3601	Loss_G: 2.0481	D(x): 0.7354	D(G(z)): 0.0367 / 0.1615
[194/1000][0/4]	Loss_D: 0.2446	Loss_G: 2.8631	D(x): 0.8326	D(G(z)): 0.0506 / 0.0758
[195/1000][0/4]	Loss_D: 0.2646	Loss_G: 3.7236	D(x): 0.9264	D(G(z)): 0.1603 / 0.0341
[196/1000][0/4]	Loss_D: 0.4845	Loss_G: 6.2827	D(x): 0.9688	D(G(z)): 0.3433 / 0.0037
[197/1000][0/4]	Loss_D: 1.3456	Loss_G: 6.2629	D(x): 0.9789	D(G(z)): 0.6646 / 0.0075
[198/1000][0/4]	Loss_D: 0.5185	Loss_G: 1.9791	D(x): 0.7320	D(G(z)): 0.1419 / 0.1899
[199/1000][0/4]	Loss_D: 0.4572	Loss_G: 2.3482	D(x): 0.6960	D(G(z)): 0.0578 / 0.1216
[200/1000][0/4]	Loss_D: 0.3971	Loss_G: 3.0835	D(x): 0.8106	D(G(z)): 0.1539 / 0.0615
[201/1000][0/4]	Loss_D: 1.0548	Loss_G: 1.9632	D(x): 0.4074	D(G(z)): 0.0109 / 0.2030
[202/1000][0/4]	Loss_D: 0.2974	Loss_G: 2.8623	D(x): 0.8789	D(G(z)): 0.1439 / 0.0777
[203/1000][0/4]	Loss_D: 0.4859	Loss_G: 1.8554	D(x): 0.6682	D(G(z)): 0.0533 / 0.1962
[204/1000][0/4]	Loss_D: 0.2209	Loss_G: 3.7317	D(x): 0.8929	D(G(z)): 0.0957 / 0.0340
[205/1000][0/4]	Loss_D: 0.3083	Loss_G: 2.9704	D(x): 0.8634	D(G(z)): 0.1391 / 0.0661
[206/1000][0/4]	Loss_D: 0.2159	Loss_G: 3.3805	D(x): 0.9178	D(G(z)): 0.1170 / 0.0429
[207/1000][0/4]	Loss_D: 0.2017	Loss_G: 3.0466	D(x): 0.9134	D(G(z)): 0.1008 / 0.0602
[208/1000][0/4]	Loss_D: 0.3454	Loss_G: 2.0816	D(x): 0.7557	D(G(z)): 0.0494 / 0.1574
[209/1000][0/4]	Loss_D: 0.2635	Loss_G: 3.5657	D(x): 0.9183	D(G(z)): 0.1557 / 0.0374
[210/1000][0/4]	Loss_D: 0.2809	Loss_G: 2.4198	D(x): 0.8316	D(G(z)): 0.0830 / 0.1181
[211/1000][0/4]	Loss_D: 0.2926	Loss_G: 2.8836	D(x): 0.8102	D(G(z)): 0.0688 / 0.0785
[212/1000][0/4]	Loss_D: 0.3045	Loss_G: 4.3193	D(x): 0.9629	D(G(z)): 0.2225 / 0.0179
[213/1000][0/4]	Loss_D: 0.5075	Loss_G: 4.8523	D(x): 0.9718	D(G(z)): 0.3498 / 0.0127
[214/1000][0/4]	Loss_D: 0.2628	Loss_G: 3.7413	D(x): 0.9680	D(G(z)): 0.1936 / 0.0329
[215/1000][0/4]	Loss_D: 0.2461	Loss_G: 2.9695	D(x): 0.8544	D(G(z)): 0.0783 / 0.0694
[216/1000][0/4]	Loss_D: 0.3997	Loss_G: 5.8158	D(x): 0.9679	D(G(z)): 0.2915 / 0.0053
[217/1000][0/4]	Loss_D: 0.4788	Loss_G: 5.3602	D(x): 0.9792	D(G(z)): 0.3176 / 0.0088
[218/1000][0/4]	Loss_D: 1.4741	Loss_G: 4.4994	D(x): 0.9929	D(G(z)): 0.6608 / 0.0237
[219/1000][0/4]	Loss_D: 0.6717	Loss_G: 1.9927	D(x): 0.6365	D(G(z)): 0.1091 / 0.1805
[220/1000][0/4]	Loss_D: 0.6911	Loss_G: 2.3736	D(x): 0.5543	D(G(z)): 0.0292 / 0.1393
[221/1000][0/4]	Loss_D: 0.4004	Loss_G: 2.8901	D(x): 0.8223	D(G(z)): 0.1658 / 0.0748
[222/1000][0/4]	Loss_D: 0.6871	Loss_G: 1.4338	D(x): 0.5575	D(G(z)): 0.0296 / 0.2823
[223/1000][0/4]	Loss_D: 0.3863	Loss_G: 3.9859	D(x): 0.9578	D(G(z)): 0.2723 / 0.0259
[224/1000][0/4]	Loss_D: 0.2963	Loss_G: 3.2701	D(x): 0.8941	D(G(z)): 0.1601 / 0.0486
[225/1000][0/4]	Loss_D: 0.3037	Loss_G: 3.5607	D(x): 0.9296	D(G(z)): 0.1943 / 0.0408
[226/1000][0/4]	Loss_D: 0.2207	Loss_G: 3.3360	D(x): 0.9080	D(G(z)): 0.1109 / 0.0480
[227/1000][0/4]	Loss_D: 0.2365	Loss_G: 3.1492	D(x): 0.8848	D(G(z)): 0.1012 / 0.0595
[228/1000][0/4]	Loss_D: 0.2772	Loss_G: 2.0817	D(x): 0.8233	D(G(z)): 0.0715 / 0.1642
[229/1000][0/4]	Loss_D: 0.2611	Loss_G: 2.7923	D(x): 0.8498	D(G(z)): 0.0845 / 0.0892
[230/1000][0/4]	Loss_D: 0.2457	Loss_G: 3.5511	D(x): 0.9342	D(G(z)): 0.1550 / 0.0382
[231/1000][0/4]	Loss_D: 0.2116	Loss_G: 3.0758	D(x): 0.9063	D(G(z)): 0.1021 / 0.0606
[232/1000][0/4]	Loss_D: 0.2543	Loss_G: 3.9233	D(x): 0.9542	D(G(z)): 0.1761 / 0.0282
[233/1000][0/4]	Loss_D: 0.8280	Loss_G: 6.5919	D(x): 0.9889	D(G(z)): 0.5042 / 0.0030
[234/1000][0/4]	Loss_D: 0.8261	Loss_G: 5.5919	D(x): 0.9932	D(G(z)): 0.5037 / 0.0066
[235/1000][0/4]	Loss_D: 0.2548	Loss_G: 3.0801	D(x): 0.8736	D(G(z)): 0.1044 / 0.0630
[236/1000][0/4]	Loss_D: 0.2717	Loss_G: 3.2120	D(x): 0.8843	D(G(z)): 0.1288 / 0.0569
[237/1000][0/4]	Loss_D: 0.2390	Loss_G: 2.9544	D(x): 0.8896	D(G(z)): 0.1081 / 0.0691
[238/1000][0/4]	Loss_D: 0.2339	Loss_G: 2.8514	D(x): 0.8597	D(G(z)): 0.0725 / 0.0743
[239/1000][0/4]	Loss_D: 0.2149	Loss_G: 3.0060	D(x): 0.8927	D(G(z)): 0.0916 / 0.0641
[240/1000][0/4]	Loss_D: 0.2032	Loss_G: 3.2894	D(x): 0.9116	D(G(z)): 0.0988 / 0.0498
[241/1000][0/4]	Loss_D: 0.2669	Loss_G: 4.4815	D(x): 0.9531	D(G(z)): 0.1874 / 0.0162
[242/1000][0/4]	Loss_D: 1.9386	Loss_G: 11.0033	D(x): 0.9963	D(G(z)): 0.8004 / 0.0000
[243/1000][0/4]	Loss_D: 0.6158	Loss_G: 3.1419	D(x): 0.9463	D(G(z)): 0.3576 / 0.0738
[244/1000][0/4]	Loss_D: 0.8310	Loss_G: 6.1417	D(x): 0.9394	D(G(z)): 0.4963 / 0.0046
[245/1000][0/4]	Loss_D: 0.3766	Loss_G: 2.1881	D(x): 0.7505	D(G(z)): 0.0594 / 0.1469
[246/1000][0/4]	Loss_D: 0.5114	Loss_G: 2.2484	D(x): 0.6920	D(G(z)): 0.0970 / 0.1381
[247/1000][0/4]	Loss_D: 0.4051	Loss_G: 2.6038	D(x): 0.7265	D(G(z)): 0.0547 / 0.0981
[248/1000][0/4]	Loss_D: 0.4064	Loss_G: 3.5948	D(x): 0.8764	D(G(z)): 0.2213 / 0.0384
[249/1000][0/4]	Loss_D: 0.4461	Loss_G: 4.2828	D(x): 0.9502	D(G(z)): 0.3055 / 0.0194
[250/1000][0/4]	Loss_D: 0.2850	Loss_G: 2.9856	D(x): 0.8262	D(G(z)): 0.0762 / 0.0710
[251/1000][0/4]	Loss_D: 0.3356	Loss_G: 4.1299	D(x): 0.9456	D(G(z)): 0.2290 / 0.0232
[252/1000][0/4]	Loss_D: 0.2319	Loss_G: 3.2421	D(x): 0.9035	D(G(z)): 0.1161 / 0.0515
[253/1000][0/4]	Loss_D: 0.2915	Loss_G: 3.5196	D(x): 0.9312	D(G(z)): 0.1865 / 0.0415
[254/1000][0/4]	Loss_D: 0.2640	Loss_G: 2.8730	D(x): 0.8782	D(G(z)): 0.1176 / 0.0738
[255/1000][0/4]	Loss_D: 0.3248	Loss_G: 3.8766	D(x): 0.9561	D(G(z)): 0.2290 / 0.0286
[256/1000][0/4]	Loss_D: 0.2419	Loss_G: 2.6468	D(x): 0.8579	D(G(z)): 0.0786 / 0.0898
[257/1000][0/4]	Loss_D: 0.2533	Loss_G: 2.6411	D(x): 0.8340	D(G(z)): 0.0611 / 0.0998
[258/1000][0/4]	Loss_D: 0.2114	Loss_G: 3.1647	D(x): 0.8756	D(G(z)): 0.0701 / 0.0596
[259/1000][0/4]	Loss_D: 0.2289	Loss_G: 3.2526	D(x): 0.9122	D(G(z)): 0.1219 / 0.0519
[260/1000][0/4]	Loss_D: 0.2190	Loss_G: 2.9376	D(x): 0.9002	D(G(z)): 0.1028 / 0.0709
[261/1000][0/4]	Loss_D: 0.2881	Loss_G: 2.0184	D(x): 0.7895	D(G(z)): 0.0404 / 0.1718
[262/1000][0/4]	Loss_D: 0.2182	Loss_G: 3.0643	D(x): 0.8384	D(G(z)): 0.0348 / 0.0657
[263/1000][0/4]	Loss_D: 0.2826	Loss_G: 4.3611	D(x): 0.9545	D(G(z)): 0.1975 / 0.0190
[264/1000][0/4]	Loss_D: 0.8386	Loss_G: 7.6109	D(x): 0.9898	D(G(z)): 0.5055 / 0.0011
[265/1000][0/4]	Loss_D: 2.6147	Loss_G: 11.6499	D(x): 0.9975	D(G(z)): 0.8752 / 0.0001
[266/1000][0/4]	Loss_D: 0.8841	Loss_G: 2.7384	D(x): 0.7386	D(G(z)): 0.3432 / 0.1031
[267/1000][0/4]	Loss_D: 0.9015	Loss_G: 5.6110	D(x): 0.9335	D(G(z)): 0.5113 / 0.0084
[268/1000][0/4]	Loss_D: 0.4849	Loss_G: 3.0185	D(x): 0.9411	D(G(z)): 0.3066 / 0.0756
[269/1000][0/4]	Loss_D: 0.4039	Loss_G: 3.0318	D(x): 0.7970	D(G(z)): 0.1392 / 0.0703
[270/1000][0/4]	Loss_D: 0.4246	Loss_G: 3.9975	D(x): 0.9038	D(G(z)): 0.2558 / 0.0260
[271/1000][0/4]	Loss_D: 0.3329	Loss_G: 2.7577	D(x): 0.8416	D(G(z)): 0.1361 / 0.0833
[272/1000][0/4]	Loss_D: 0.2863	Loss_G: 2.9463	D(x): 0.8264	D(G(z)): 0.0798 / 0.0710
[273/1000][0/4]	Loss_D: 0.2609	Loss_G: 2.7029	D(x): 0.8425	D(G(z)): 0.0778 / 0.0885
[274/1000][0/4]	Loss_D: 0.3834	Loss_G: 2.7665	D(x): 0.7309	D(G(z)): 0.0484 / 0.0958
[275/1000][0/4]	Loss_D: 0.2359	Loss_G: 2.9236	D(x): 0.8995	D(G(z)): 0.1156 / 0.0693
[276/1000][0/4]	Loss_D: 0.2690	Loss_G: 2.7714	D(x): 0.8661	D(G(z)): 0.1098 / 0.0830
[277/1000][0/4]	Loss_D: 0.2494	Loss_G: 3.1851	D(x): 0.8363	D(G(z)): 0.0607 / 0.0594
[278/1000][0/4]	Loss_D: 0.2471	Loss_G: 2.9686	D(x): 0.9060	D(G(z)): 0.1312 / 0.0670
[279/1000][0/4]	Loss_D: 0.3917	Loss_G: 4.6255	D(x): 0.9585	D(G(z)): 0.2782 / 0.0140
[280/1000][0/4]	Loss_D: 0.1973	Loss_G: 2.7095	D(x): 0.8821	D(G(z)): 0.0633 / 0.0912
[281/1000][0/4]	Loss_D: 0.2768	Loss_G: 3.9687	D(x): 0.9507	D(G(z)): 0.1890 / 0.0275
[282/1000][0/4]	Loss_D: 0.2323	Loss_G: 2.8852	D(x): 0.8436	D(G(z)): 0.0528 / 0.0740
[283/1000][0/4]	Loss_D: 0.2294	Loss_G: 2.9188	D(x): 0.8843	D(G(z)): 0.0950 / 0.0725
[284/1000][0/4]	Loss_D: 0.3089	Loss_G: 4.3567	D(x): 0.9533	D(G(z)): 0.2179 / 0.0184
[285/1000][0/4]	Loss_D: 0.1961	Loss_G: 3.1136	D(x): 0.9334	D(G(z)): 0.1144 / 0.0587
[286/1000][0/4]	Loss_D: 0.2278	Loss_G: 2.5471	D(x): 0.8645	D(G(z)): 0.0713 / 0.1030
[287/1000][0/4]	Loss_D: 0.3631	Loss_G: 2.0761	D(x): 0.7243	D(G(z)): 0.0223 / 0.1684
[288/1000][0/4]	Loss_D: 0.2402	Loss_G: 2.8460	D(x): 0.8582	D(G(z)): 0.0775 / 0.0774
[289/1000][0/4]	Loss_D: 0.6037	Loss_G: 6.6981	D(x): 0.9799	D(G(z)): 0.4131 / 0.0023
[290/1000][0/4]	Loss_D: 1.7911	Loss_G: 7.4561	D(x): 0.9951	D(G(z)): 0.7612 / 0.0016
[291/1000][0/4]	Loss_D: 0.4199	Loss_G: 2.9089	D(x): 0.9390	D(G(z)): 0.2614 / 0.0877
[292/1000][0/4]	Loss_D: 0.6545	Loss_G: 1.9033	D(x): 0.6088	D(G(z)): 0.0741 / 0.1984
[293/1000][0/4]	Loss_D: 0.3066	Loss_G: 3.4130	D(x): 0.9062	D(G(z)): 0.1732 / 0.0461
[294/1000][0/4]	Loss_D: 0.3053	Loss_G: 2.9706	D(x): 0.8622	D(G(z)): 0.1331 / 0.0688
[295/1000][0/4]	Loss_D: 0.2711	Loss_G: 2.5180	D(x): 0.8653	D(G(z)): 0.1098 / 0.1005
[296/1000][0/4]	Loss_D: 0.2585	Loss_G: 3.7271	D(x): 0.9381	D(G(z)): 0.1674 / 0.0337
[297/1000][0/4]	Loss_D: 0.2318	Loss_G: 2.6222	D(x): 0.8596	D(G(z)): 0.0706 / 0.0952
[298/1000][0/4]	Loss_D: 0.3529	Loss_G: 4.3887	D(x): 0.9592	D(G(z)): 0.2504 / 0.0183
[299/1000][0/4]	Loss_D: 0.2345	Loss_G: 2.5007	D(x): 0.8458	D(G(z)): 0.0565 / 0.1106
[300/1000][0/4]	Loss_D: 0.2817	Loss_G: 2.3148	D(x): 0.8382	D(G(z)): 0.0910 / 0.1293
[301/1000][0/4]	Loss_D: 0.2505	Loss_G: 3.4925	D(x): 0.9138	D(G(z)): 0.1392 / 0.0408
[302/1000][0/4]	Loss_D: 0.2316	Loss_G: 3.8494	D(x): 0.9418	D(G(z)): 0.1514 / 0.0290
[303/1000][0/4]	Loss_D: 0.2221	Loss_G: 3.6135	D(x): 0.9426	D(G(z)): 0.1436 / 0.0376
[304/1000][0/4]	Loss_D: 0.2280	Loss_G: 3.3787	D(x): 0.9098	D(G(z)): 0.1182 / 0.0466
[305/1000][0/4]	Loss_D: 0.2323	Loss_G: 4.2471	D(x): 0.9610	D(G(z)): 0.1658 / 0.0205
[306/1000][0/4]	Loss_D: 0.4358	Loss_G: 1.6471	D(x): 0.6731	D(G(z)): 0.0138 / 0.2359
[307/1000][0/4]	Loss_D: 1.3311	Loss_G: 0.4563	D(x): 0.3353	D(G(z)): 0.0052 / 0.6993
[308/1000][0/4]	Loss_D: 0.1942	Loss_G: 4.8511	D(x): 0.9398	D(G(z)): 0.1117 / 0.0150
[309/1000][0/4]	Loss_D: 0.2977	Loss_G: 3.3320	D(x): 0.8939	D(G(z)): 0.1554 / 0.0538
[310/1000][0/4]	Loss_D: 0.4294	Loss_G: 4.3553	D(x): 0.9319	D(G(z)): 0.2789 / 0.0190
[311/1000][0/4]	Loss_D: 0.2648	Loss_G: 2.4971	D(x): 0.8177	D(G(z)): 0.0520 / 0.1054
[312/1000][0/4]	Loss_D: 0.3395	Loss_G: 4.4355	D(x): 0.9703	D(G(z)): 0.2495 / 0.0171
[313/1000][0/4]	Loss_D: 0.2398	Loss_G: 4.4170	D(x): 0.9451	D(G(z)): 0.1577 / 0.0178
[314/1000][0/4]	Loss_D: 0.2129	Loss_G: 2.5913	D(x): 0.8966	D(G(z)): 0.0905 / 0.1155
[315/1000][0/4]	Loss_D: 0.1964	Loss_G: 3.8001	D(x): 0.9380	D(G(z)): 0.1164 / 0.0347
[316/1000][0/4]	Loss_D: 0.2653	Loss_G: 3.3417	D(x): 0.9005	D(G(z)): 0.1362 / 0.0522
[317/1000][0/4]	Loss_D: 0.3482	Loss_G: 4.6230	D(x): 0.9616	D(G(z)): 0.2473 / 0.0157
[318/1000][0/4]	Loss_D: 0.1686	Loss_G: 3.6156	D(x): 0.9165	D(G(z)): 0.0736 / 0.0420
[319/1000][0/4]	Loss_D: 0.2734	Loss_G: 3.8274	D(x): 0.9444	D(G(z)): 0.1819 / 0.0324
[320/1000][0/4]	Loss_D: 0.2786	Loss_G: 2.0005	D(x): 0.8100	D(G(z)): 0.0566 / 0.1727
[321/1000][0/4]	Loss_D: 0.2999	Loss_G: 4.6503	D(x): 0.9752	D(G(z)): 0.2209 / 0.0149
[322/1000][0/4]	Loss_D: 0.1646	Loss_G: 3.5322	D(x): 0.9344	D(G(z)): 0.0879 / 0.0407
[323/1000][0/4]	Loss_D: 0.1913	Loss_G: 3.5090	D(x): 0.9454	D(G(z)): 0.1211 / 0.0414
[324/1000][0/4]	Loss_D: 0.1874	Loss_G: 3.4287	D(x): 0.9359	D(G(z)): 0.1092 / 0.0447
[325/1000][0/4]	Loss_D: 0.1765	Loss_G: 3.1781	D(x): 0.9278	D(G(z)): 0.0928 / 0.0560
[326/1000][0/4]	Loss_D: 0.2254	Loss_G: 3.7930	D(x): 0.9499	D(G(z)): 0.1496 / 0.0319
[327/1000][0/4]	Loss_D: 0.1796	Loss_G: 3.2208	D(x): 0.9168	D(G(z)): 0.0843 / 0.0557
[328/1000][0/4]	Loss_D: 0.1737	Loss_G: 3.2141	D(x): 0.9249	D(G(z)): 0.0877 / 0.0542
[329/1000][0/4]	Loss_D: 0.5842	Loss_G: 1.0885	D(x): 0.5908	D(G(z)): 0.0078 / 0.3875
[330/1000][0/4]	Loss_D: 5.8499	Loss_G: 0.9999	D(x): 0.0066	D(G(z)): 0.0004 / 0.4800
[331/1000][0/4]	Loss_D: 1.6829	Loss_G: 0.9038	D(x): 0.2768	D(G(z)): 0.0177 / 0.5138
[332/1000][0/4]	Loss_D: 0.8429	Loss_G: 1.8165	D(x): 0.5455	D(G(z)): 0.0990 / 0.2227
[333/1000][0/4]	Loss_D: 0.4484	Loss_G: 3.2772	D(x): 0.7708	D(G(z)): 0.1398 / 0.0622
[334/1000][0/4]	Loss_D: 0.7480	Loss_G: 5.1627	D(x): 0.9283	D(G(z)): 0.4456 / 0.0106
[335/1000][0/4]	Loss_D: 0.4411	Loss_G: 2.0494	D(x): 0.7158	D(G(z)): 0.0694 / 0.1731
[336/1000][0/4]	Loss_D: 0.3978	Loss_G: 3.3445	D(x): 0.9113	D(G(z)): 0.2425 / 0.0492
[337/1000][0/4]	Loss_D: 0.3031	Loss_G: 2.9784	D(x): 0.8564	D(G(z)): 0.1261 / 0.0696
[338/1000][0/4]	Loss_D: 0.3270	Loss_G: 3.3986	D(x): 0.9156	D(G(z)): 0.1963 / 0.0501
[339/1000][0/4]	Loss_D: 0.3416	Loss_G: 4.0271	D(x): 0.9318	D(G(z)): 0.2189 / 0.0319
[340/1000][0/4]	Loss_D: 0.3958	Loss_G: 3.8909	D(x): 0.9523	D(G(z)): 0.2711 / 0.0350
[341/1000][0/4]	Loss_D: 0.2769	Loss_G: 2.9627	D(x): 0.8301	D(G(z)): 0.0749 / 0.0799
[342/1000][0/4]	Loss_D: 0.2442	Loss_G: 3.1756	D(x): 0.9083	D(G(z)): 0.1294 / 0.0569
[343/1000][0/4]	Loss_D: 0.3300	Loss_G: 4.0667	D(x): 0.9506	D(G(z)): 0.2268 / 0.0260
[344/1000][0/4]	Loss_D: 0.2733	Loss_G: 2.4721	D(x): 0.8213	D(G(z)): 0.0637 / 0.1183
[345/1000][0/4]	Loss_D: 0.2352	Loss_G: 2.7764	D(x): 0.8709	D(G(z)): 0.0860 / 0.0864
[346/1000][0/4]	Loss_D: 0.2225	Loss_G: 3.3509	D(x): 0.9267	D(G(z)): 0.1295 / 0.0489
[347/1000][0/4]	Loss_D: 0.2409	Loss_G: 3.6357	D(x): 0.9345	D(G(z)): 0.1511 / 0.0394
[348/1000][0/4]	Loss_D: 0.2557	Loss_G: 4.0205	D(x): 0.9607	D(G(z)): 0.1833 / 0.0258
[349/1000][0/4]	Loss_D: 0.2274	Loss_G: 2.6073	D(x): 0.8719	D(G(z)): 0.0793 / 0.0999
[350/1000][0/4]	Loss_D: 0.2543	Loss_G: 4.0799	D(x): 0.9543	D(G(z)): 0.1759 / 0.0243
[351/1000][0/4]	Loss_D: 0.5841	Loss_G: 4.7157	D(x): 0.9823	D(G(z)): 0.3933 / 0.0134
[352/1000][0/4]	Loss_D: 0.2246	Loss_G: 3.1973	D(x): 0.8610	D(G(z)): 0.0642 / 0.0606
[353/1000][0/4]	Loss_D: 0.2081	Loss_G: 2.9892	D(x): 0.9046	D(G(z)): 0.0966 / 0.0683
[354/1000][0/4]	Loss_D: 0.2181	Loss_G: 2.7239	D(x): 0.8501	D(G(z)): 0.0484 / 0.0946
[355/1000][0/4]	Loss_D: 0.2109	Loss_G: 3.1862	D(x): 0.9166	D(G(z)): 0.1100 / 0.0585
[356/1000][0/4]	Loss_D: 0.1781	Loss_G: 3.3706	D(x): 0.9096	D(G(z)): 0.0763 / 0.0476
[357/1000][0/4]	Loss_D: 0.3275	Loss_G: 4.1168	D(x): 0.9738	D(G(z)): 0.2428 / 0.0245
[358/1000][0/4]	Loss_D: 0.2000	Loss_G: 3.2654	D(x): 0.9117	D(G(z)): 0.0973 / 0.0520
[359/1000][0/4]	Loss_D: 0.2116	Loss_G: 2.7958	D(x): 0.8802	D(G(z)): 0.0745 / 0.0825
[360/1000][0/4]	Loss_D: 0.2711	Loss_G: 2.3705	D(x): 0.8098	D(G(z)): 0.0480 / 0.1333
[361/1000][0/4]	Loss_D: 0.1773	Loss_G: 3.4290	D(x): 0.9086	D(G(z)): 0.0738 / 0.0470
[362/1000][0/4]	Loss_D: 0.2342	Loss_G: 4.3620	D(x): 0.9609	D(G(z)): 0.1650 / 0.0198
[363/1000][0/4]	Loss_D: 0.1813	Loss_G: 2.9426	D(x): 0.9509	D(G(z)): 0.1161 / 0.0797
[364/1000][0/4]	Loss_D: 0.2087	Loss_G: 3.9946	D(x): 0.9607	D(G(z)): 0.1443 / 0.0271
[365/1000][0/4]	Loss_D: 0.1834	Loss_G: 3.3759	D(x): 0.9318	D(G(z)): 0.0991 / 0.0492
[366/1000][0/4]	Loss_D: 0.2108	Loss_G: 2.7620	D(x): 0.8884	D(G(z)): 0.0823 / 0.0875
[367/1000][0/4]	Loss_D: 0.1754	Loss_G: 3.4108	D(x): 0.9185	D(G(z)): 0.0825 / 0.0467
[368/1000][0/4]	Loss_D: 0.2042	Loss_G: 2.3664	D(x): 0.8600	D(G(z)): 0.0468 / 0.1297
[369/1000][0/4]	Loss_D: 0.1625	Loss_G: 3.2913	D(x): 0.9131	D(G(z)): 0.0661 / 0.0514
[370/1000][0/4]	Loss_D: 0.1467	Loss_G: 3.6162	D(x): 0.9322	D(G(z)): 0.0704 / 0.0400
[371/1000][0/4]	Loss_D: 0.5585	Loss_G: 6.4963	D(x): 0.9860	D(G(z)): 0.3885 / 0.0027
[372/1000][0/4]	Loss_D: 2.0842	Loss_G: 10.8389	D(x): 0.9967	D(G(z)): 0.8053 / 0.0002
[373/1000][0/4]	Loss_D: 2.1511	Loss_G: 5.4795	D(x): 0.9812	D(G(z)): 0.7713 / 0.0137
[374/1000][0/4]	Loss_D: 0.6372	Loss_G: 2.2474	D(x): 0.6956	D(G(z)): 0.1778 / 0.1506
[375/1000][0/4]	Loss_D: 0.4710	Loss_G: 2.5356	D(x): 0.7788	D(G(z)): 0.1648 / 0.1068
[376/1000][0/4]	Loss_D: 0.7690	Loss_G: 1.5542	D(x): 0.5489	D(G(z)): 0.0556 / 0.2914
[377/1000][0/4]	Loss_D: 0.3451	Loss_G: 3.2431	D(x): 0.8730	D(G(z)): 0.1710 / 0.0609
[378/1000][0/4]	Loss_D: 0.2866	Loss_G: 3.1279	D(x): 0.8960	D(G(z)): 0.1495 / 0.0634
[379/1000][0/4]	Loss_D: 0.2827	Loss_G: 3.0150	D(x): 0.8586	D(G(z)): 0.1113 / 0.0676
[380/1000][0/4]	Loss_D: 0.3477	Loss_G: 3.0841	D(x): 0.9009	D(G(z)): 0.2001 / 0.0680
[381/1000][0/4]	Loss_D: 0.2700	Loss_G: 3.3691	D(x): 0.9033	D(G(z)): 0.1459 / 0.0482
[382/1000][0/4]	Loss_D: 0.2571	Loss_G: 3.5369	D(x): 0.9340	D(G(z)): 0.1611 / 0.0441
[383/1000][0/4]	Loss_D: 0.2813	Loss_G: 3.9981	D(x): 0.9538	D(G(z)): 0.1954 / 0.0292
[384/1000][0/4]	Loss_D: 0.2185	Loss_G: 2.7782	D(x): 0.8883	D(G(z)): 0.0880 / 0.0857
[385/1000][0/4]	Loss_D: 0.2414	Loss_G: 3.3066	D(x): 0.9210	D(G(z)): 0.1388 / 0.0526
[386/1000][0/4]	Loss_D: 0.2227	Loss_G: 2.5815	D(x): 0.8546	D(G(z)): 0.0579 / 0.1004
[387/1000][0/4]	Loss_D: 0.2410	Loss_G: 3.6740	D(x): 0.9332	D(G(z)): 0.1481 / 0.0372
[388/1000][0/4]	Loss_D: 0.2366	Loss_G: 2.6065	D(x): 0.8417	D(G(z)): 0.0548 / 0.1026
[389/1000][0/4]	Loss_D: 0.2796	Loss_G: 3.7826	D(x): 0.9459	D(G(z)): 0.1882 / 0.0346
[390/1000][0/4]	Loss_D: 0.2267	Loss_G: 3.3220	D(x): 0.9388	D(G(z)): 0.1426 / 0.0513
[391/1000][0/4]	Loss_D: 0.1841	Loss_G: 3.1949	D(x): 0.9183	D(G(z)): 0.0897 / 0.0578
[392/1000][0/4]	Loss_D: 0.2007	Loss_G: 3.3071	D(x): 0.9107	D(G(z)): 0.0962 / 0.0512
[393/1000][0/4]	Loss_D: 0.2076	Loss_G: 3.2687	D(x): 0.9078	D(G(z)): 0.0991 / 0.0560
[394/1000][0/4]	Loss_D: 0.1854	Loss_G: 3.2163	D(x): 0.9347	D(G(z)): 0.1059 / 0.0550
[395/1000][0/4]	Loss_D: 0.2012	Loss_G: 3.6380	D(x): 0.9357	D(G(z)): 0.1191 / 0.0366
[396/1000][0/4]	Loss_D: 0.1762	Loss_G: 3.5999	D(x): 0.9510	D(G(z)): 0.1122 / 0.0379
[397/1000][0/4]	Loss_D: 0.1765	Loss_G: 3.2151	D(x): 0.9255	D(G(z)): 0.0908 / 0.0536
[398/1000][0/4]	Loss_D: 0.1897	Loss_G: 3.8004	D(x): 0.9597	D(G(z)): 0.1314 / 0.0316
[399/1000][0/4]	Loss_D: 0.2170	Loss_G: 4.0429	D(x): 0.9659	D(G(z)): 0.1574 / 0.0240
[400/1000][0/4]	Loss_D: 0.1819	Loss_G: 3.0555	D(x): 0.9136	D(G(z)): 0.0838 / 0.0638
[401/1000][0/4]	Loss_D: 0.1523	Loss_G: 3.4006	D(x): 0.9262	D(G(z)): 0.0701 / 0.0472
[402/1000][0/4]	Loss_D: 0.5784	Loss_G: 6.3977	D(x): 0.9886	D(G(z)): 0.3928 / 0.0035
[403/1000][0/4]	Loss_D: 2.8264	Loss_G: 9.9560	D(x): 0.9989	D(G(z)): 0.8738 / 0.0003
[404/1000][0/4]	Loss_D: 1.1764	Loss_G: 4.3006	D(x): 0.9412	D(G(z)): 0.5700 / 0.0371
[405/1000][0/4]	Loss_D: 0.5634	Loss_G: 2.5757	D(x): 0.7062	D(G(z)): 0.1402 / 0.1124
[406/1000][0/4]	Loss_D: 0.4485	Loss_G: 3.2336	D(x): 0.8449	D(G(z)): 0.2185 / 0.0631
[407/1000][0/4]	Loss_D: 0.3118	Loss_G: 3.2698	D(x): 0.8684	D(G(z)): 0.1422 / 0.0591
[408/1000][0/4]	Loss_D: 0.3192	Loss_G: 3.3781	D(x): 0.8543	D(G(z)): 0.1318 / 0.0527
[409/1000][0/4]	Loss_D: 0.2484	Loss_G: 3.3983	D(x): 0.8993	D(G(z)): 0.1231 / 0.0478
[410/1000][0/4]	Loss_D: 0.2312	Loss_G: 2.9570	D(x): 0.8822	D(G(z)): 0.0924 / 0.0715
[411/1000][0/4]	Loss_D: 0.2061	Loss_G: 3.0612	D(x): 0.8960	D(G(z)): 0.0857 / 0.0648
[412/1000][0/4]	Loss_D: 0.2435	Loss_G: 3.7733	D(x): 0.9296	D(G(z)): 0.1490 / 0.0321
[413/1000][0/4]	Loss_D: 0.1874	Loss_G: 3.1911	D(x): 0.9058	D(G(z)): 0.0802 / 0.0571
[414/1000][0/4]	Loss_D: 0.2342	Loss_G: 3.5584	D(x): 0.9443	D(G(z)): 0.1531 / 0.0410
[415/1000][0/4]	Loss_D: 0.1910	Loss_G: 3.2233	D(x): 0.8820	D(G(z)): 0.0586 / 0.0606
[416/1000][0/4]	Loss_D: 0.1922	Loss_G: 3.0925	D(x): 0.9088	D(G(z)): 0.0874 / 0.0651
[417/1000][0/4]	Loss_D: 0.1735	Loss_G: 3.2496	D(x): 0.9277	D(G(z)): 0.0898 / 0.0532
[418/1000][0/4]	Loss_D: 0.1976	Loss_G: 3.2077	D(x): 0.9115	D(G(z)): 0.0944 / 0.0569
[419/1000][0/4]	Loss_D: 0.5399	Loss_G: 5.3870	D(x): 0.9858	D(G(z)): 0.3759 / 0.0067
[420/1000][0/4]	Loss_D: 0.1625	Loss_G: 3.6314	D(x): 0.8970	D(G(z)): 0.0467 / 0.0366
[421/1000][0/4]	Loss_D: 0.1936	Loss_G: 3.2854	D(x): 0.9220	D(G(z)): 0.1006 / 0.0542
[422/1000][0/4]	Loss_D: 0.1638	Loss_G: 3.4574	D(x): 0.9356	D(G(z)): 0.0890 / 0.0451
[423/1000][0/4]	Loss_D: 0.1690	Loss_G: 3.3514	D(x): 0.9169	D(G(z)): 0.0753 / 0.0512
[424/1000][0/4]	Loss_D: 0.2537	Loss_G: 4.2904	D(x): 0.9672	D(G(z)): 0.1848 / 0.0198
[425/1000][0/4]	Loss_D: 0.1663	Loss_G: 3.2441	D(x): 0.9061	D(G(z)): 0.0601 / 0.0562
[426/1000][0/4]	Loss_D: 0.1624	Loss_G: 3.4036	D(x): 0.9391	D(G(z)): 0.0901 / 0.0481
[427/1000][0/4]	Loss_D: 0.1431	Loss_G: 3.5336	D(x): 0.9216	D(G(z)): 0.0573 / 0.0417
[428/1000][0/4]	Loss_D: 0.1622	Loss_G: 3.1974	D(x): 0.9061	D(G(z)): 0.0576 / 0.0576
[429/1000][0/4]	Loss_D: 0.1577	Loss_G: 3.5477	D(x): 0.9546	D(G(z)): 0.1005 / 0.0408
[430/1000][0/4]	Loss_D: 0.1445	Loss_G: 3.5899	D(x): 0.9397	D(G(z)): 0.0754 / 0.0412
[431/1000][0/4]	Loss_D: 0.1577	Loss_G: 2.8801	D(x): 0.9123	D(G(z)): 0.0608 / 0.0783
[432/1000][0/4]	Loss_D: 0.1448	Loss_G: 3.1847	D(x): 0.9363	D(G(z)): 0.0733 / 0.0555
[433/1000][0/4]	Loss_D: 0.1471	Loss_G: 3.3530	D(x): 0.9148	D(G(z)): 0.0527 / 0.0505
[434/1000][0/4]	Loss_D: 0.1385	Loss_G: 3.2150	D(x): 0.9265	D(G(z)): 0.0571 / 0.0572
[435/1000][0/4]	Loss_D: 0.1521	Loss_G: 3.1429	D(x): 0.9174	D(G(z)): 0.0608 / 0.0610
[436/1000][0/4]	Loss_D: 0.1527	Loss_G: 3.1535	D(x): 0.9108	D(G(z)): 0.0540 / 0.0620
[437/1000][0/4]	Loss_D: 0.1420	Loss_G: 3.5860	D(x): 0.9475	D(G(z)): 0.0810 / 0.0383
[438/1000][0/4]	Loss_D: 0.1089	Loss_G: 3.4164	D(x): 0.9373	D(G(z)): 0.0416 / 0.0465
[439/1000][0/4]	Loss_D: 0.2388	Loss_G: 2.2203	D(x): 0.8152	D(G(z)): 0.0240 / 0.1453
[440/1000][0/4]	Loss_D: 0.1564	Loss_G: 3.9140	D(x): 0.9519	D(G(z)): 0.0950 / 0.0316
[441/1000][0/4]	Loss_D: 0.1351	Loss_G: 3.5920	D(x): 0.9164	D(G(z)): 0.0434 / 0.0416
[442/1000][0/4]	Loss_D: 0.1638	Loss_G: 3.4322	D(x): 0.9286	D(G(z)): 0.0817 / 0.0472
[443/1000][0/4]	Loss_D: 0.1467	Loss_G: 3.4515	D(x): 0.9426	D(G(z)): 0.0800 / 0.0484
[444/1000][0/4]	Loss_D: 0.1436	Loss_G: 3.6643	D(x): 0.9540	D(G(z)): 0.0892 / 0.0350
[445/1000][0/4]	Loss_D: 0.1356	Loss_G: 3.0913	D(x): 0.9270	D(G(z)): 0.0558 / 0.0631
[446/1000][0/4]	Loss_D: 0.1496	Loss_G: 3.9161	D(x): 0.9588	D(G(z)): 0.0966 / 0.0289
[447/1000][0/4]	Loss_D: 0.1220	Loss_G: 3.2804	D(x): 0.9383	D(G(z)): 0.0543 / 0.0561
[448/1000][0/4]	Loss_D: 0.4421	Loss_G: 6.4666	D(x): 0.9904	D(G(z)): 0.3182 / 0.0025
[449/1000][0/4]	Loss_D: 3.5845	Loss_G: 8.1937	D(x): 0.9980	D(G(z)): 0.8892 / 0.0014
[450/1000][0/4]	Loss_D: 0.6295	Loss_G: 2.5324	D(x): 0.8177	D(G(z)): 0.2800 / 0.1273
[451/1000][0/4]	Loss_D: 0.5433	Loss_G: 2.0046	D(x): 0.7201	D(G(z)): 0.1386 / 0.1950
[452/1000][0/4]	Loss_D: 0.4322	Loss_G: 3.0124	D(x): 0.7364	D(G(z)): 0.0761 / 0.0792
[453/1000][0/4]	Loss_D: 0.4267	Loss_G: 3.8957	D(x): 0.9253	D(G(z)): 0.2654 / 0.0302
[454/1000][0/4]	Loss_D: 0.2772	Loss_G: 2.6331	D(x): 0.8284	D(G(z)): 0.0728 / 0.0997
[455/1000][0/4]	Loss_D: 0.2136	Loss_G: 3.2024	D(x): 0.8967	D(G(z)): 0.0929 / 0.0588
[456/1000][0/4]	Loss_D: 0.2139	Loss_G: 3.1566	D(x): 0.8856	D(G(z)): 0.0814 / 0.0637
[457/1000][0/4]	Loss_D: 0.2049	Loss_G: 3.6480	D(x): 0.9412	D(G(z)): 0.1264 / 0.0380
[458/1000][0/4]	Loss_D: 0.1750	Loss_G: 3.6321	D(x): 0.9466	D(G(z)): 0.1068 / 0.0382
[459/1000][0/4]	Loss_D: 0.1594	Loss_G: 3.5494	D(x): 0.9410	D(G(z)): 0.0893 / 0.0428
[460/1000][0/4]	Loss_D: 0.2728	Loss_G: 4.0335	D(x): 0.9552	D(G(z)): 0.1882 / 0.0269
[461/1000][0/4]	Loss_D: 0.1722	Loss_G: 3.6274	D(x): 0.9305	D(G(z)): 0.0900 / 0.0391
[462/1000][0/4]	Loss_D: 0.1785	Loss_G: 3.6446	D(x): 0.9462	D(G(z)): 0.1100 / 0.0375
[463/1000][0/4]	Loss_D: 0.1620	Loss_G: 3.3995	D(x): 0.9241	D(G(z)): 0.0757 / 0.0477
[464/1000][0/4]	Loss_D: 0.1452	Loss_G: 3.2313	D(x): 0.9254	D(G(z)): 0.0627 / 0.0565
[465/1000][0/4]	Loss_D: 0.2563	Loss_G: 4.3656	D(x): 0.9708	D(G(z)): 0.1886 / 0.0202
[466/1000][0/4]	Loss_D: 0.1487	Loss_G: 3.7191	D(x): 0.9532	D(G(z)): 0.0912 / 0.0351
[467/1000][0/4]	Loss_D: 0.1653	Loss_G: 3.4402	D(x): 0.9436	D(G(z)): 0.0954 / 0.0460
[468/1000][0/4]	Loss_D: 0.1411	Loss_G: 3.4009	D(x): 0.9250	D(G(z)): 0.0582 / 0.0495
[469/1000][0/4]	Loss_D: 0.1755	Loss_G: 3.0389	D(x): 0.8739	D(G(z)): 0.0365 / 0.0700
[470/1000][0/4]	Loss_D: 0.1398	Loss_G: 3.0891	D(x): 0.9307	D(G(z)): 0.0630 / 0.0659
[471/1000][0/4]	Loss_D: 0.1585	Loss_G: 3.3351	D(x): 0.8826	D(G(z)): 0.0301 / 0.0524
[472/1000][0/4]	Loss_D: 0.1637	Loss_G: 3.8067	D(x): 0.9711	D(G(z)): 0.1208 / 0.0319
[473/1000][0/4]	Loss_D: 0.1411	Loss_G: 3.3103	D(x): 0.9343	D(G(z)): 0.0669 / 0.0534
[474/1000][0/4]	Loss_D: 0.1518	Loss_G: 3.6733	D(x): 0.9608	D(G(z)): 0.1018 / 0.0352
[475/1000][0/4]	Loss_D: 0.1447	Loss_G: 3.4204	D(x): 0.9277	D(G(z)): 0.0647 / 0.0483
[476/1000][0/4]	Loss_D: 0.1141	Loss_G: 3.3526	D(x): 0.9294	D(G(z)): 0.0385 / 0.0483
[477/1000][0/4]	Loss_D: 0.1306	Loss_G: 3.7866	D(x): 0.9528	D(G(z)): 0.0753 / 0.0329
[478/1000][0/4]	Loss_D: 0.1473	Loss_G: 3.7879	D(x): 0.9614	D(G(z)): 0.0971 / 0.0323
[479/1000][0/4]	Loss_D: 0.1166	Loss_G: 3.3846	D(x): 0.9332	D(G(z)): 0.0448 / 0.0499
[480/1000][0/4]	Loss_D: 0.1476	Loss_G: 3.2074	D(x): 0.9006	D(G(z)): 0.0387 / 0.0614
[481/1000][0/4]	Loss_D: 0.1316	Loss_G: 3.3710	D(x): 0.9399	D(G(z)): 0.0639 / 0.0510
[482/1000][0/4]	Loss_D: 0.1791	Loss_G: 2.1522	D(x): 0.8733	D(G(z)): 0.0379 / 0.1554
[483/1000][0/4]	Loss_D: 0.1079	Loss_G: 3.9369	D(x): 0.9580	D(G(z)): 0.0609 / 0.0286
[484/1000][0/4]	Loss_D: 0.1046	Loss_G: 3.6031	D(x): 0.9503	D(G(z)): 0.0502 / 0.0408
[485/1000][0/4]	Loss_D: 0.1029	Loss_G: 3.5570	D(x): 0.9545	D(G(z)): 0.0532 / 0.0395
[486/1000][0/4]	Loss_D: 0.1107	Loss_G: 3.6425	D(x): 0.9549	D(G(z)): 0.0603 / 0.0379
[487/1000][0/4]	Loss_D: 0.1064	Loss_G: 3.7927	D(x): 0.9577	D(G(z)): 0.0596 / 0.0314
[488/1000][0/4]	Loss_D: 0.1177	Loss_G: 3.6014	D(x): 0.9496	D(G(z)): 0.0609 / 0.0387
[489/1000][0/4]	Loss_D: 0.1056	Loss_G: 3.7400	D(x): 0.9389	D(G(z)): 0.0402 / 0.0342
[490/1000][0/4]	Loss_D: 0.1424	Loss_G: 3.9625	D(x): 0.9686	D(G(z)): 0.0988 / 0.0282
[491/1000][0/4]	Loss_D: 0.1000	Loss_G: 3.6759	D(x): 0.9601	D(G(z)): 0.0558 / 0.0363
[492/1000][0/4]	Loss_D: 0.1069	Loss_G: 3.7634	D(x): 0.9538	D(G(z)): 0.0562 / 0.0328
[493/1000][0/4]	Loss_D: 0.1178	Loss_G: 4.3304	D(x): 0.9727	D(G(z)): 0.0827 / 0.0192
[494/1000][0/4]	Loss_D: 0.1205	Loss_G: 4.0919	D(x): 0.9659	D(G(z)): 0.0795 / 0.0238
[495/1000][0/4]	Loss_D: 0.1055	Loss_G: 3.6405	D(x): 0.9527	D(G(z)): 0.0536 / 0.0389
[496/1000][0/4]	Loss_D: 0.1019	Loss_G: 3.7558	D(x): 0.9526	D(G(z)): 0.0504 / 0.0345
[497/1000][0/4]	Loss_D: 0.0949	Loss_G: 3.9690	D(x): 0.9657	D(G(z)): 0.0565 / 0.0270
[498/1000][0/4]	Loss_D: 0.2500	Loss_G: 5.7996	D(x): 0.9891	D(G(z)): 0.1951 / 0.0046
[499/1000][0/4]	Loss_D: 2.3112	Loss_G: 12.8323	D(x): 0.9978	D(G(z)): 0.7993 / 0.0000
[500/1000][0/4]	Loss_D: 2.4066	Loss_G: 3.9579	D(x): 0.9754	D(G(z)): 0.8157 / 0.0496
[501/1000][0/4]	Loss_D: 0.7282	Loss_G: 3.6775	D(x): 0.8549	D(G(z)): 0.3863 / 0.0423
[502/1000][0/4]	Loss_D: 0.7969	Loss_G: 4.1598	D(x): 0.9254	D(G(z)): 0.4409 / 0.0277
[503/1000][0/4]	Loss_D: 0.4018	Loss_G: 2.8602	D(x): 0.8197	D(G(z)): 0.1553 / 0.0838
[504/1000][0/4]	Loss_D: 0.3114	Loss_G: 3.3877	D(x): 0.8993	D(G(z)): 0.1698 / 0.0516
[505/1000][0/4]	Loss_D: 0.2976	Loss_G: 3.2104	D(x): 0.8771	D(G(z)): 0.1385 / 0.0597
[506/1000][0/4]	Loss_D: 0.2653	Loss_G: 3.4772	D(x): 0.8833	D(G(z)): 0.1162 / 0.0547
[507/1000][0/4]	Loss_D: 0.2150	Loss_G: 3.4933	D(x): 0.9281	D(G(z)): 0.1228 / 0.0449
[508/1000][0/4]	Loss_D: 0.2149	Loss_G: 3.0380	D(x): 0.8883	D(G(z)): 0.0846 / 0.0714
[509/1000][0/4]	Loss_D: 0.2138	Loss_G: 2.9628	D(x): 0.8905	D(G(z)): 0.0865 / 0.0747
[510/1000][0/4]	Loss_D: 0.1833	Loss_G: 3.3355	D(x): 0.9205	D(G(z)): 0.0895 / 0.0479
[511/1000][0/4]	Loss_D: 0.2272	Loss_G: 4.0937	D(x): 0.9563	D(G(z)): 0.1565 / 0.0240
[512/1000][0/4]	Loss_D: 0.1500	Loss_G: 3.6305	D(x): 0.9221	D(G(z)): 0.0629 / 0.0411
[513/1000][0/4]	Loss_D: 0.1565	Loss_G: 3.4406	D(x): 0.9368	D(G(z)): 0.0835 / 0.0468
[514/1000][0/4]	Loss_D: 0.1664	Loss_G: 3.6256	D(x): 0.9503	D(G(z)): 0.1052 / 0.0376
[515/1000][0/4]	Loss_D: 0.1715	Loss_G: 3.0269	D(x): 0.8795	D(G(z)): 0.0377 / 0.0727
[516/1000][0/4]	Loss_D: 0.2189	Loss_G: 4.0772	D(x): 0.9627	D(G(z)): 0.1538 / 0.0256
[517/1000][0/4]	Loss_D: 0.1714	Loss_G: 3.1573	D(x): 0.8862	D(G(z)): 0.0445 / 0.0619
[518/1000][0/4]	Loss_D: 0.1729	Loss_G: 3.6668	D(x): 0.9624	D(G(z)): 0.1206 / 0.0362
[519/1000][0/4]	Loss_D: 0.1314	Loss_G: 3.4095	D(x): 0.9343	D(G(z)): 0.0595 / 0.0452
[520/1000][0/4]	Loss_D: 0.1539	Loss_G: 2.9602	D(x): 0.8894	D(G(z)): 0.0328 / 0.0733
[521/1000][0/4]	Loss_D: 0.1142	Loss_G: 3.2299	D(x): 0.9318	D(G(z)): 0.0412 / 0.0557
[522/1000][0/4]	Loss_D: 0.1427	Loss_G: 3.7608	D(x): 0.9675	D(G(z)): 0.0993 / 0.0329
[523/1000][0/4]	Loss_D: 0.1141	Loss_G: 3.5824	D(x): 0.9428	D(G(z)): 0.0521 / 0.0377
[524/1000][0/4]	Loss_D: 0.1086	Loss_G: 3.3249	D(x): 0.9375	D(G(z)): 0.0418 / 0.0512
[525/1000][0/4]	Loss_D: 0.1396	Loss_G: 4.0861	D(x): 0.9727	D(G(z)): 0.1015 / 0.0233
[526/1000][0/4]	Loss_D: 0.1077	Loss_G: 4.0103	D(x): 0.9528	D(G(z)): 0.0555 / 0.0268
[527/1000][0/4]	Loss_D: 0.1139	Loss_G: 3.2017	D(x): 0.9370	D(G(z)): 0.0461 / 0.0571
[528/1000][0/4]	Loss_D: 0.1025	Loss_G: 3.5140	D(x): 0.9317	D(G(z)): 0.0300 / 0.0433
[529/1000][0/4]	Loss_D: 0.1350	Loss_G: 4.0127	D(x): 0.9751	D(G(z)): 0.0999 / 0.0248
[530/1000][0/4]	Loss_D: 0.1273	Loss_G: 4.0840	D(x): 0.9687	D(G(z)): 0.0871 / 0.0243
[531/1000][0/4]	Loss_D: 0.1210	Loss_G: 3.5228	D(x): 0.9425	D(G(z)): 0.0570 / 0.0442
[532/1000][0/4]	Loss_D: 0.1247	Loss_G: 3.7006	D(x): 0.9684	D(G(z)): 0.0857 / 0.0342
[533/1000][0/4]	Loss_D: 0.1079	Loss_G: 3.3960	D(x): 0.9364	D(G(z)): 0.0395 / 0.0462
[534/1000][0/4]	Loss_D: 0.1069	Loss_G: 3.5931	D(x): 0.9581	D(G(z)): 0.0601 / 0.0385
[535/1000][0/4]	Loss_D: 0.1056	Loss_G: 3.6947	D(x): 0.9568	D(G(z)): 0.0570 / 0.0346
[536/1000][0/4]	Loss_D: 0.1723	Loss_G: 5.0902	D(x): 0.9879	D(G(z)): 0.1392 / 0.0089
[537/1000][0/4]	Loss_D: 0.0949	Loss_G: 3.6986	D(x): 0.9415	D(G(z)): 0.0328 / 0.0366
[538/1000][0/4]	Loss_D: 0.1263	Loss_G: 4.0841	D(x): 0.9767	D(G(z)): 0.0932 / 0.0247
[539/1000][0/4]	Loss_D: 0.0983	Loss_G: 4.0797	D(x): 0.9672	D(G(z)): 0.0604 / 0.0263
[540/1000][0/4]	Loss_D: 0.1017	Loss_G: 3.6994	D(x): 0.9623	D(G(z)): 0.0593 / 0.0361
[541/1000][0/4]	Loss_D: 0.0858	Loss_G: 4.0671	D(x): 0.9691	D(G(z)): 0.0515 / 0.0250
[542/1000][0/4]	Loss_D: 0.0967	Loss_G: 4.4529	D(x): 0.9828	D(G(z)): 0.0740 / 0.0166
[543/1000][0/4]	Loss_D: 0.0940	Loss_G: 3.8895	D(x): 0.9683	D(G(z)): 0.0582 / 0.0300
[544/1000][0/4]	Loss_D: 0.0985	Loss_G: 3.4727	D(x): 0.9473	D(G(z)): 0.0422 / 0.0428
[545/1000][0/4]	Loss_D: 0.0836	Loss_G: 3.8911	D(x): 0.9646	D(G(z)): 0.0453 / 0.0291
[546/1000][0/4]	Loss_D: 0.1203	Loss_G: 2.7334	D(x): 0.9079	D(G(z)): 0.0221 / 0.0896
[547/1000][0/4]	Loss_D: 0.0926	Loss_G: 3.8551	D(x): 0.9749	D(G(z)): 0.0629 / 0.0298
[548/1000][0/4]	Loss_D: 0.0803	Loss_G: 3.7342	D(x): 0.9523	D(G(z)): 0.0301 / 0.0334
[549/1000][0/4]	Loss_D: 0.0795	Loss_G: 3.8182	D(x): 0.9660	D(G(z)): 0.0430 / 0.0316
[550/1000][0/4]	Loss_D: 0.0860	Loss_G: 4.0332	D(x): 0.9746	D(G(z)): 0.0566 / 0.0257
[551/1000][0/4]	Loss_D: 0.0834	Loss_G: 3.9555	D(x): 0.9697	D(G(z)): 0.0501 / 0.0263
[552/1000][0/4]	Loss_D: 0.0839	Loss_G: 4.0074	D(x): 0.9681	D(G(z)): 0.0490 / 0.0259
[553/1000][0/4]	Loss_D: 0.0935	Loss_G: 3.9271	D(x): 0.9609	D(G(z)): 0.0507 / 0.0288
[554/1000][0/4]	Loss_D: 0.0836	Loss_G: 3.9646	D(x): 0.9693	D(G(z)): 0.0500 / 0.0265
[555/1000][0/4]	Loss_D: 0.1121	Loss_G: 4.7950	D(x): 0.9834	D(G(z)): 0.0875 / 0.0119
[556/1000][0/4]	Loss_D: 0.0774	Loss_G: 3.7767	D(x): 0.9520	D(G(z)): 0.0266 / 0.0350
[557/1000][0/4]	Loss_D: 0.0803	Loss_G: 3.8075	D(x): 0.9665	D(G(z)): 0.0442 / 0.0312
[558/1000][0/4]	Loss_D: 0.0847	Loss_G: 3.6067	D(x): 0.9491	D(G(z)): 0.0306 / 0.0395
[559/1000][0/4]	Loss_D: 0.1064	Loss_G: 4.6054	D(x): 0.9841	D(G(z)): 0.0836 / 0.0139
[560/1000][0/4]	Loss_D: 0.0801	Loss_G: 3.7207	D(x): 0.9477	D(G(z)): 0.0249 / 0.0355
[561/1000][0/4]	Loss_D: 0.0681	Loss_G: 3.8686	D(x): 0.9671	D(G(z)): 0.0333 / 0.0295
[562/1000][0/4]	Loss_D: 0.0917	Loss_G: 4.2415	D(x): 0.9649	D(G(z)): 0.0525 / 0.0219
[563/1000][0/4]	Loss_D: 0.0867	Loss_G: 4.4279	D(x): 0.9768	D(G(z)): 0.0594 / 0.0171
[564/1000][0/4]	Loss_D: 0.0606	Loss_G: 4.2244	D(x): 0.9736	D(G(z)): 0.0327 / 0.0207
[565/1000][0/4]	Loss_D: 0.0764	Loss_G: 3.6625	D(x): 0.9454	D(G(z)): 0.0187 / 0.0379
[566/1000][0/4]	Loss_D: 0.0733	Loss_G: 4.0129	D(x): 0.9684	D(G(z)): 0.0395 / 0.0258
[567/1000][0/4]	Loss_D: 0.1020	Loss_G: 3.2566	D(x): 0.9171	D(G(z)): 0.0143 / 0.0553
[568/1000][0/4]	Loss_D: 0.1388	Loss_G: 4.9622	D(x): 0.9857	D(G(z)): 0.1113 / 0.0095
[569/1000][0/4]	Loss_D: 0.1134	Loss_G: 3.2089	D(x): 0.9108	D(G(z)): 0.0176 / 0.0643
[570/1000][0/4]	Loss_D: 0.0818	Loss_G: 3.9329	D(x): 0.9713	D(G(z)): 0.0500 / 0.0284
[571/1000][0/4]	Loss_D: 0.0643	Loss_G: 4.0497	D(x): 0.9660	D(G(z)): 0.0286 / 0.0270
[572/1000][0/4]	Loss_D: 0.1134	Loss_G: 5.1584	D(x): 0.9887	D(G(z)): 0.0933 / 0.0086
[573/1000][0/4]	Loss_D: 0.0781	Loss_G: 4.2457	D(x): 0.9840	D(G(z)): 0.0581 / 0.0207
[574/1000][0/4]	Loss_D: 0.0674	Loss_G: 4.0909	D(x): 0.9620	D(G(z)): 0.0274 / 0.0255
[575/1000][0/4]	Loss_D: 0.1224	Loss_G: 4.8760	D(x): 0.9864	D(G(z)): 0.0973 / 0.0113
[576/1000][0/4]	Loss_D: 0.0904	Loss_G: 4.4071	D(x): 0.9814	D(G(z)): 0.0668 / 0.0180
[577/1000][0/4]	Loss_D: 0.0567	Loss_G: 4.2795	D(x): 0.9805	D(G(z)): 0.0356 / 0.0193
[578/1000][0/4]	Loss_D: 0.0694	Loss_G: 4.1845	D(x): 0.9692	D(G(z)): 0.0361 / 0.0228
[579/1000][0/4]	Loss_D: 0.0682	Loss_G: 4.2626	D(x): 0.9727	D(G(z)): 0.0388 / 0.0204
[580/1000][0/4]	Loss_D: 0.0911	Loss_G: 4.1454	D(x): 0.9737	D(G(z)): 0.0604 / 0.0233
[581/1000][0/4]	Loss_D: 0.0539	Loss_G: 4.1521	D(x): 0.9702	D(G(z)): 0.0230 / 0.0254
[582/1000][0/4]	Loss_D: 0.0509	Loss_G: 4.5053	D(x): 0.9793	D(G(z)): 0.0290 / 0.0169
[583/1000][0/4]	Loss_D: 0.0804	Loss_G: 4.2841	D(x): 0.9562	D(G(z)): 0.0339 / 0.0220
[584/1000][0/4]	Loss_D: 0.0862	Loss_G: 3.3346	D(x): 0.9415	D(G(z)): 0.0247 / 0.0535
[585/1000][0/4]	Loss_D: 0.0673	Loss_G: 4.3900	D(x): 0.9749	D(G(z)): 0.0399 / 0.0188
[586/1000][0/4]	Loss_D: 0.0892	Loss_G: 3.1442	D(x): 0.9284	D(G(z)): 0.0137 / 0.0614
[587/1000][0/4]	Loss_D: 0.0707	Loss_G: 4.3638	D(x): 0.9752	D(G(z)): 0.0433 / 0.0201
[588/1000][0/4]	Loss_D: 0.0668	Loss_G: 3.9185	D(x): 0.9673	D(G(z)): 0.0325 / 0.0279
[589/1000][0/4]	Loss_D: 0.0658	Loss_G: 3.9041	D(x): 0.9580	D(G(z)): 0.0220 / 0.0319
[590/1000][0/4]	Loss_D: 0.0726	Loss_G: 4.3509	D(x): 0.9862	D(G(z)): 0.0554 / 0.0179
[591/1000][0/4]	Loss_D: 0.0801	Loss_G: 4.8759	D(x): 0.9838	D(G(z)): 0.0600 / 0.0119
[592/1000][0/4]	Loss_D: 0.0949	Loss_G: 4.5951	D(x): 0.9828	D(G(z)): 0.0722 / 0.0149
[593/1000][0/4]	Loss_D: 0.1412	Loss_G: 2.6833	D(x): 0.8793	D(G(z)): 0.0098 / 0.1092
[594/1000][0/4]	Loss_D: 0.0859	Loss_G: 4.3291	D(x): 0.9380	D(G(z)): 0.0202 / 0.0210
[595/1000][0/4]	Loss_D: 0.0648	Loss_G: 3.8516	D(x): 0.9631	D(G(z)): 0.0264 / 0.0310
[596/1000][0/4]	Loss_D: 0.0719	Loss_G: 3.6020	D(x): 0.9599	D(G(z)): 0.0296 / 0.0422
[597/1000][0/4]	Loss_D: 0.1108	Loss_G: 4.6247	D(x): 0.9905	D(G(z)): 0.0930 / 0.0135
[598/1000][0/4]	Loss_D: 0.0555	Loss_G: 4.4186	D(x): 0.9727	D(G(z)): 0.0269 / 0.0186
[599/1000][0/4]	Loss_D: 0.0830	Loss_G: 4.7939	D(x): 0.9879	D(G(z)): 0.0665 / 0.0126
[600/1000][0/4]	Loss_D: 0.0602	Loss_G: 4.3365	D(x): 0.9686	D(G(z)): 0.0274 / 0.0197
[601/1000][0/4]	Loss_D: 0.0530	Loss_G: 4.2122	D(x): 0.9754	D(G(z)): 0.0273 / 0.0218
[602/1000][0/4]	Loss_D: 0.0679	Loss_G: 3.7030	D(x): 0.9565	D(G(z)): 0.0226 / 0.0393
[603/1000][0/4]	Loss_D: 0.0641	Loss_G: 4.2812	D(x): 0.9800	D(G(z)): 0.0413 / 0.0203
[604/1000][0/4]	Loss_D: 0.0591	Loss_G: 4.8602	D(x): 0.9794	D(G(z)): 0.0365 / 0.0122
[605/1000][0/4]	Loss_D: 0.0893	Loss_G: 4.0765	D(x): 0.9807	D(G(z)): 0.0626 / 0.0256
[606/1000][0/4]	Loss_D: 0.0775	Loss_G: 4.6088	D(x): 0.9904	D(G(z)): 0.0639 / 0.0139
[607/1000][0/4]	Loss_D: 0.0748	Loss_G: 4.7156	D(x): 0.9799	D(G(z)): 0.0515 / 0.0140
[608/1000][0/4]	Loss_D: 0.0935	Loss_G: 5.0876	D(x): 0.9878	D(G(z)): 0.0755 / 0.0088
[609/1000][0/4]	Loss_D: 0.0776	Loss_G: 4.7982	D(x): 0.9821	D(G(z)): 0.0554 / 0.0125
[610/1000][0/4]	Loss_D: 0.0633	Loss_G: 4.3795	D(x): 0.9825	D(G(z)): 0.0435 / 0.0186
[611/1000][0/4]	Loss_D: 0.0642	Loss_G: 4.3447	D(x): 0.9712	D(G(z)): 0.0334 / 0.0199
[612/1000][0/4]	Loss_D: 0.0721	Loss_G: 4.6963	D(x): 0.9866	D(G(z)): 0.0547 / 0.0135
[613/1000][0/4]	Loss_D: 0.0574	Loss_G: 4.2039	D(x): 0.9769	D(G(z)): 0.0325 / 0.0237
[614/1000][0/4]	Loss_D: 0.0608	Loss_G: 3.7991	D(x): 0.9586	D(G(z)): 0.0180 / 0.0336
[615/1000][0/4]	Loss_D: 0.0581	Loss_G: 4.0309	D(x): 0.9722	D(G(z)): 0.0284 / 0.0277
[616/1000][0/4]	Loss_D: 0.1187	Loss_G: 5.6928	D(x): 0.9915	D(G(z)): 0.0998 / 0.0052
[617/1000][0/4]	Loss_D: 2.3085	Loss_G: 23.4455	D(x): 0.9709	D(G(z)): 0.7974 / 0.0000
[618/1000][0/4]	Loss_D: 37.1179	Loss_G: 1.4654	D(x): 0.9441	D(G(z)): 0.8124 / 0.6307
[619/1000][0/4]	Loss_D: 6.2669	Loss_G: 1.1541	D(x): 0.0935	D(G(z)): 0.1558 / 0.4369
[620/1000][0/4]	Loss_D: 2.4404	Loss_G: 1.8916	D(x): 0.6320	D(G(z)): 0.4702 / 0.3609
[621/1000][0/4]	Loss_D: 1.7130	Loss_G: 1.8265	D(x): 0.7064	D(G(z)): 0.6117 / 0.2396
[622/1000][0/4]	Loss_D: 1.2610	Loss_G: 0.9213	D(x): 0.5360	D(G(z)): 0.3867 / 0.4342
[623/1000][0/4]	Loss_D: 1.4637	Loss_G: 0.6433	D(x): 0.4073	D(G(z)): 0.3362 / 0.5533
[624/1000][0/4]	Loss_D: 1.3871	Loss_G: 0.7779	D(x): 0.4475	D(G(z)): 0.3269 / 0.4906
[625/1000][0/4]	Loss_D: 1.3843	Loss_G: 0.6040	D(x): 0.4428	D(G(z)): 0.3520 / 0.5751
[626/1000][0/4]	Loss_D: 1.4692	Loss_G: 0.9172	D(x): 0.4425	D(G(z)): 0.3889 / 0.4394
[627/1000][0/4]	Loss_D: 1.2792	Loss_G: 0.9262	D(x): 0.5150	D(G(z)): 0.3967 / 0.4284
[628/1000][0/4]	Loss_D: 1.3468	Loss_G: 1.0053	D(x): 0.3713	D(G(z)): 0.2055 / 0.4086
[629/1000][0/4]	Loss_D: 1.4236	Loss_G: 1.3644	D(x): 0.5307	D(G(z)): 0.5023 / 0.2952
[630/1000][0/4]	Loss_D: 1.2777	Loss_G: 1.4880	D(x): 0.6566	D(G(z)): 0.5315 / 0.2729
[631/1000][0/4]	Loss_D: 1.2844	Loss_G: 1.7368	D(x): 0.6754	D(G(z)): 0.5445 / 0.2199
[632/1000][0/4]	Loss_D: 1.7226	Loss_G: 2.0914	D(x): 0.6847	D(G(z)): 0.6782 / 0.1712
[633/1000][0/4]	Loss_D: 1.1959	Loss_G: 1.8118	D(x): 0.6621	D(G(z)): 0.4772 / 0.2117
[634/1000][0/4]	Loss_D: 1.1115	Loss_G: 1.2959	D(x): 0.5955	D(G(z)): 0.3775 / 0.3160
[635/1000][0/4]	Loss_D: 1.5178	Loss_G: 0.5599	D(x): 0.3612	D(G(z)): 0.2642 / 0.6022
[636/1000][0/4]	Loss_D: 1.2527	Loss_G: 0.9252	D(x): 0.4828	D(G(z)): 0.3503 / 0.4251
[637/1000][0/4]	Loss_D: 1.0298	Loss_G: 1.7641	D(x): 0.6815	D(G(z)): 0.4388 / 0.2161
[638/1000][0/4]	Loss_D: 1.2894	Loss_G: 2.2212	D(x): 0.7569	D(G(z)): 0.5758 / 0.1504
[639/1000][0/4]	Loss_D: 1.4113	Loss_G: 2.3746	D(x): 0.8050	D(G(z)): 0.6420 / 0.1336
[640/1000][0/4]	Loss_D: 1.0734	Loss_G: 1.7679	D(x): 0.6428	D(G(z)): 0.4186 / 0.2136
[641/1000][0/4]	Loss_D: 1.2497	Loss_G: 2.5207	D(x): 0.7175	D(G(z)): 0.5402 / 0.1160
[642/1000][0/4]	Loss_D: 1.1276	Loss_G: 1.6823	D(x): 0.8318	D(G(z)): 0.5619 / 0.2453
[643/1000][0/4]	Loss_D: 1.0055	Loss_G: 1.0810	D(x): 0.5210	D(G(z)): 0.2143 / 0.3807
[644/1000][0/4]	Loss_D: 1.3025	Loss_G: 1.0637	D(x): 0.3996	D(G(z)): 0.2081 / 0.3916
[645/1000][0/4]	Loss_D: 1.0403	Loss_G: 1.9477	D(x): 0.6147	D(G(z)): 0.3560 / 0.1990
[646/1000][0/4]	Loss_D: 1.3240	Loss_G: 3.8886	D(x): 0.8825	D(G(z)): 0.6250 / 0.0441
[647/1000][0/4]	Loss_D: 0.9296	Loss_G: 1.8023	D(x): 0.6299	D(G(z)): 0.3108 / 0.2092
[648/1000][0/4]	Loss_D: 0.7886	Loss_G: 2.6680	D(x): 0.7303	D(G(z)): 0.3229 / 0.1077
[649/1000][0/4]	Loss_D: 1.2958	Loss_G: 5.9975	D(x): 0.9274	D(G(z)): 0.5905 / 0.0101
[650/1000][0/4]	Loss_D: 0.7561	Loss_G: 2.4671	D(x): 0.7471	D(G(z)): 0.3063 / 0.1314
[651/1000][0/4]	Loss_D: 1.0477	Loss_G: 1.3186	D(x): 0.4976	D(G(z)): 0.1183 / 0.3549
[652/1000][0/4]	Loss_D: 0.6241	Loss_G: 3.2562	D(x): 0.7537	D(G(z)): 0.2337 / 0.0824
[653/1000][0/4]	Loss_D: 1.3199	Loss_G: 7.9641	D(x): 0.9431	D(G(z)): 0.6190 / 0.0041
[654/1000][0/4]	Loss_D: 0.6503	Loss_G: 2.2746	D(x): 0.7603	D(G(z)): 0.2637 / 0.1626
[655/1000][0/4]	Loss_D: 0.9441	Loss_G: 1.5296	D(x): 0.5188	D(G(z)): 0.1217 / 0.2930
[656/1000][0/4]	Loss_D: 0.5145	Loss_G: 3.6486	D(x): 0.7827	D(G(z)): 0.1920 / 0.0556
[657/1000][0/4]	Loss_D: 0.5013	Loss_G: 3.9099	D(x): 0.8503	D(G(z)): 0.2465 / 0.0382
[658/1000][0/4]	Loss_D: 0.7015	Loss_G: 7.1483	D(x): 0.9296	D(G(z)): 0.4020 / 0.0033
[659/1000][0/4]	Loss_D: 0.7253	Loss_G: 3.2341	D(x): 0.8731	D(G(z)): 0.3227 / 0.1012
[660/1000][0/4]	Loss_D: 0.4567	Loss_G: 3.6891	D(x): 0.8447	D(G(z)): 0.1954 / 0.0459
[661/1000][0/4]	Loss_D: 0.4594	Loss_G: 3.4353	D(x): 0.7991	D(G(z)): 0.1638 / 0.0603
[662/1000][0/4]	Loss_D: 0.3663	Loss_G: 3.4540	D(x): 0.8268	D(G(z)): 0.1281 / 0.0567
[663/1000][0/4]	Loss_D: 0.3449	Loss_G: 3.4982	D(x): 0.8253	D(G(z)): 0.1125 / 0.0562
[664/1000][0/4]	Loss_D: 0.3440	Loss_G: 4.8794	D(x): 0.8750	D(G(z)): 0.1476 / 0.0185
[665/1000][0/4]	Loss_D: 0.3450	Loss_G: 5.3334	D(x): 0.9377	D(G(z)): 0.2134 / 0.0103
[666/1000][0/4]	Loss_D: 0.5519	Loss_G: 5.6151	D(x): 0.9683	D(G(z)): 0.3454 / 0.0073
[667/1000][0/4]	Loss_D: 0.2347	Loss_G: 3.9420	D(x): 0.8785	D(G(z)): 0.0840 / 0.0386
[668/1000][0/4]	Loss_D: 0.2138	Loss_G: 3.1503	D(x): 0.8937	D(G(z)): 0.0879 / 0.0667
[669/1000][0/4]	Loss_D: 0.2401	Loss_G: 4.5297	D(x): 0.9457	D(G(z)): 0.1537 / 0.0200
[670/1000][0/4]	Loss_D: 0.2115	Loss_G: 3.0892	D(x): 0.8726	D(G(z)): 0.0615 / 0.0713
[671/1000][0/4]	Loss_D: 0.1788	Loss_G: 4.2222	D(x): 0.9286	D(G(z)): 0.0918 / 0.0247
[672/1000][0/4]	Loss_D: 0.1850	Loss_G: 3.4585	D(x): 0.9111	D(G(z)): 0.0806 / 0.0523
[673/1000][0/4]	Loss_D: 0.1790	Loss_G: 3.9151	D(x): 0.9172	D(G(z)): 0.0809 / 0.0359
[674/1000][0/4]	Loss_D: 0.1467	Loss_G: 3.5292	D(x): 0.9214	D(G(z)): 0.0588 / 0.0449
[675/1000][0/4]	Loss_D: 0.2430	Loss_G: 5.1059	D(x): 0.9666	D(G(z)): 0.1746 / 0.0102
[676/1000][0/4]	Loss_D: 0.1539	Loss_G: 3.2296	D(x): 0.9013	D(G(z)): 0.0428 / 0.0665
[677/1000][0/4]	Loss_D: 0.1346	Loss_G: 4.1418	D(x): 0.9502	D(G(z)): 0.0758 / 0.0258
[678/1000][0/4]	Loss_D: 0.1327	Loss_G: 3.6985	D(x): 0.9362	D(G(z)): 0.0620 / 0.0385
[679/1000][0/4]	Loss_D: 0.1421	Loss_G: 3.8422	D(x): 0.9565	D(G(z)): 0.0882 / 0.0334
[680/1000][0/4]	Loss_D: 0.1617	Loss_G: 3.1846	D(x): 0.8828	D(G(z)): 0.0310 / 0.0668
[681/1000][0/4]	Loss_D: 0.1336	Loss_G: 3.8619	D(x): 0.9616	D(G(z)): 0.0866 / 0.0299
[682/1000][0/4]	Loss_D: 0.1158	Loss_G: 4.1430	D(x): 0.9638	D(G(z)): 0.0731 / 0.0238
[683/1000][0/4]	Loss_D: 0.1396	Loss_G: 3.6592	D(x): 0.9350	D(G(z)): 0.0673 / 0.0390
[684/1000][0/4]	Loss_D: 0.1365	Loss_G: 4.3576	D(x): 0.9622	D(G(z)): 0.0898 / 0.0196
[685/1000][0/4]	Loss_D: 0.1332	Loss_G: 3.7607	D(x): 0.9607	D(G(z)): 0.0840 / 0.0373
[686/1000][0/4]	Loss_D: 0.1082	Loss_G: 3.5345	D(x): 0.9293	D(G(z)): 0.0324 / 0.0444
[687/1000][0/4]	Loss_D: 0.1169	Loss_G: 4.1619	D(x): 0.9638	D(G(z)): 0.0739 / 0.0244
[688/1000][0/4]	Loss_D: 0.1349	Loss_G: 3.1253	D(x): 0.9183	D(G(z)): 0.0451 / 0.0662
[689/1000][0/4]	Loss_D: 0.1615	Loss_G: 2.5450	D(x): 0.8708	D(G(z)): 0.0197 / 0.1124
[690/1000][0/4]	Loss_D: 0.1614	Loss_G: 2.5384	D(x): 0.8890	D(G(z)): 0.0354 / 0.1166
[691/1000][0/4]	Loss_D: 1.0159	Loss_G: 1.2229	D(x): 0.4454	D(G(z)): 0.0120 / 0.4248
[692/1000][0/4]	Loss_D: 2.2488	Loss_G: 1.2048	D(x): 0.2149	D(G(z)): 0.0578 / 0.4122
[693/1000][0/4]	Loss_D: 1.2322	Loss_G: 3.6310	D(x): 0.8279	D(G(z)): 0.5742 / 0.0623
[694/1000][0/4]	Loss_D: 1.1132	Loss_G: 4.0353	D(x): 0.9286	D(G(z)): 0.5688 / 0.0328
[695/1000][0/4]	Loss_D: 0.6160	Loss_G: 3.1747	D(x): 0.8205	D(G(z)): 0.2761 / 0.0717
[696/1000][0/4]	Loss_D: 1.3362	Loss_G: 7.3792	D(x): 0.9855	D(G(z)): 0.6246 / 0.0029
[697/1000][0/4]	Loss_D: 0.4412	Loss_G: 3.6820	D(x): 0.7300	D(G(z)): 0.0455 / 0.0528
[698/1000][0/4]	Loss_D: 0.4679	Loss_G: 4.4263	D(x): 0.9460	D(G(z)): 0.2805 / 0.0229
[699/1000][0/4]	Loss_D: 0.2939	Loss_G: 3.4852	D(x): 0.8683	D(G(z)): 0.1186 / 0.0521
[700/1000][0/4]	Loss_D: 0.2252	Loss_G: 3.8260	D(x): 0.8529	D(G(z)): 0.0503 / 0.0421
[701/1000][0/4]	Loss_D: 0.1666	Loss_G: 4.1410	D(x): 0.9417	D(G(z)): 0.0943 / 0.0284
[702/1000][0/4]	Loss_D: 0.2016	Loss_G: 4.9026	D(x): 0.9654	D(G(z)): 0.1416 / 0.0122
[703/1000][0/4]	Loss_D: 0.1507	Loss_G: 3.9423	D(x): 0.9301	D(G(z)): 0.0701 / 0.0328
[704/1000][0/4]	Loss_D: 0.2038	Loss_G: 4.7406	D(x): 0.9676	D(G(z)): 0.1454 / 0.0143
[705/1000][0/4]	Loss_D: 0.1432	Loss_G: 3.9305	D(x): 0.9330	D(G(z)): 0.0661 / 0.0319
[706/1000][0/4]	Loss_D: 0.1538	Loss_G: 3.3908	D(x): 0.8990	D(G(z)): 0.0415 / 0.0539
[707/1000][0/4]	Loss_D: 0.1546	Loss_G: 4.1893	D(x): 0.9621	D(G(z)): 0.1046 / 0.0223
[708/1000][0/4]	Loss_D: 0.1140	Loss_G: 4.2218	D(x): 0.9617	D(G(z)): 0.0695 / 0.0216
[709/1000][0/4]	Loss_D: 0.1339	Loss_G: 3.8694	D(x): 0.9508	D(G(z)): 0.0760 / 0.0308
[710/1000][0/4]	Loss_D: 0.1107	Loss_G: 3.8384	D(x): 0.9492	D(G(z)): 0.0545 / 0.0343
[711/1000][0/4]	Loss_D: 0.1424	Loss_G: 3.3632	D(x): 0.8997	D(G(z)): 0.0324 / 0.0562
[712/1000][0/4]	Loss_D: 0.2375	Loss_G: 5.8140	D(x): 0.9873	D(G(z)): 0.1855 / 0.0048
[713/1000][0/4]	Loss_D: 0.1059	Loss_G: 3.9965	D(x): 0.9352	D(G(z)): 0.0353 / 0.0302
[714/1000][0/4]	Loss_D: 0.1050	Loss_G: 3.9983	D(x): 0.9654	D(G(z)): 0.0655 / 0.0263
[715/1000][0/4]	Loss_D: 0.1001	Loss_G: 3.9771	D(x): 0.9540	D(G(z)): 0.0494 / 0.0284
[716/1000][0/4]	Loss_D: 0.1148	Loss_G: 3.3653	D(x): 0.9235	D(G(z)): 0.0327 / 0.0500
[717/1000][0/4]	Loss_D: 0.0881	Loss_G: 3.7472	D(x): 0.9548	D(G(z)): 0.0399 / 0.0353
[718/1000][0/4]	Loss_D: 0.1145	Loss_G: 4.2234	D(x): 0.9640	D(G(z)): 0.0722 / 0.0223
[719/1000][0/4]	Loss_D: 0.0998	Loss_G: 3.9118	D(x): 0.9356	D(G(z)): 0.0313 / 0.0311
[720/1000][0/4]	Loss_D: 0.0898	Loss_G: 3.8220	D(x): 0.9525	D(G(z)): 0.0392 / 0.0322
[721/1000][0/4]	Loss_D: 0.0961	Loss_G: 4.0393	D(x): 0.9544	D(G(z)): 0.0464 / 0.0275
[722/1000][0/4]	Loss_D: 0.1130	Loss_G: 4.1678	D(x): 0.9656	D(G(z)): 0.0721 / 0.0229
[723/1000][0/4]	Loss_D: 0.1128	Loss_G: 4.7681	D(x): 0.9770	D(G(z)): 0.0816 / 0.0135
[724/1000][0/4]	Loss_D: 0.1097	Loss_G: 3.3411	D(x): 0.9154	D(G(z)): 0.0194 / 0.0524
[725/1000][0/4]	Loss_D: 0.1035	Loss_G: 3.9882	D(x): 0.9750	D(G(z)): 0.0725 / 0.0280
[726/1000][0/4]	Loss_D: 0.0701	Loss_G: 4.3974	D(x): 0.9730	D(G(z)): 0.0408 / 0.0196
[727/1000][0/4]	Loss_D: 0.0753	Loss_G: 3.9309	D(x): 0.9607	D(G(z)): 0.0336 / 0.0295
[728/1000][0/4]	Loss_D: 0.0787	Loss_G: 3.8163	D(x): 0.9548	D(G(z)): 0.0309 / 0.0319
[729/1000][0/4]	Loss_D: 0.0904	Loss_G: 3.4071	D(x): 0.9379	D(G(z)): 0.0247 / 0.0511
[730/1000][0/4]	Loss_D: 0.0864	Loss_G: 3.8260	D(x): 0.9596	D(G(z)): 0.0428 / 0.0319
[731/1000][0/4]	Loss_D: 0.0721	Loss_G: 4.1639	D(x): 0.9771	D(G(z)): 0.0468 / 0.0217
[732/1000][0/4]	Loss_D: 0.0769	Loss_G: 3.6465	D(x): 0.9578	D(G(z)): 0.0325 / 0.0377
[733/1000][0/4]	Loss_D: 0.0766	Loss_G: 3.9777	D(x): 0.9669	D(G(z)): 0.0411 / 0.0273
[734/1000][0/4]	Loss_D: 0.0816	Loss_G: 4.4218	D(x): 0.9793	D(G(z)): 0.0573 / 0.0174
[735/1000][0/4]	Loss_D: 0.0730	Loss_G: 3.7686	D(x): 0.9485	D(G(z)): 0.0193 / 0.0345
[736/1000][0/4]	Loss_D: 0.0720	Loss_G: 3.9850	D(x): 0.9625	D(G(z)): 0.0324 / 0.0271
[737/1000][0/4]	Loss_D: 0.0641	Loss_G: 4.2270	D(x): 0.9705	D(G(z)): 0.0331 / 0.0219
[738/1000][0/4]	Loss_D: 0.0807	Loss_G: 4.4169	D(x): 0.9809	D(G(z)): 0.0575 / 0.0183
[739/1000][0/4]	Loss_D: 0.0747	Loss_G: 4.0417	D(x): 0.9696	D(G(z)): 0.0417 / 0.0256
[740/1000][0/4]	Loss_D: 0.0652	Loss_G: 4.1263	D(x): 0.9788	D(G(z)): 0.0419 / 0.0233
[741/1000][0/4]	Loss_D: 0.0976	Loss_G: 3.2245	D(x): 0.9243	D(G(z)): 0.0176 / 0.0621
[742/1000][0/4]	Loss_D: 0.0836	Loss_G: 4.3690	D(x): 0.9860	D(G(z)): 0.0654 / 0.0180
[743/1000][0/4]	Loss_D: 0.0703	Loss_G: 4.3043	D(x): 0.9791	D(G(z)): 0.0464 / 0.0199
[744/1000][0/4]	Loss_D: 0.0735	Loss_G: 4.4262	D(x): 0.9782	D(G(z)): 0.0489 / 0.0174
[745/1000][0/4]	Loss_D: 0.0557	Loss_G: 4.3838	D(x): 0.9746	D(G(z)): 0.0289 / 0.0191
[746/1000][0/4]	Loss_D: 0.0687	Loss_G: 4.4896	D(x): 0.9826	D(G(z)): 0.0486 / 0.0170
[747/1000][0/4]	Loss_D: 0.0750	Loss_G: 4.5512	D(x): 0.9863	D(G(z)): 0.0578 / 0.0153
[748/1000][0/4]	Loss_D: 0.0556	Loss_G: 4.7950	D(x): 0.9759	D(G(z)): 0.0301 / 0.0128
[749/1000][0/4]	Loss_D: 0.0678	Loss_G: 4.2147	D(x): 0.9708	D(G(z)): 0.0366 / 0.0213
[750/1000][0/4]	Loss_D: 0.0610	Loss_G: 4.1668	D(x): 0.9727	D(G(z)): 0.0322 / 0.0228
[751/1000][0/4]	Loss_D: 0.0811	Loss_G: 3.6760	D(x): 0.9462	D(G(z)): 0.0237 / 0.0385
[752/1000][0/4]	Loss_D: 0.0638	Loss_G: 3.6421	D(x): 0.9619	D(G(z)): 0.0242 / 0.0379
[753/1000][0/4]	Loss_D: 0.0709	Loss_G: 4.3339	D(x): 0.9829	D(G(z)): 0.0511 / 0.0190
[754/1000][0/4]	Loss_D: 0.0913	Loss_G: 5.0556	D(x): 0.9856	D(G(z)): 0.0714 / 0.0095
[755/1000][0/4]	Loss_D: 0.0522	Loss_G: 4.5490	D(x): 0.9738	D(G(z)): 0.0249 / 0.0162
[756/1000][0/4]	Loss_D: 0.0544	Loss_G: 4.5119	D(x): 0.9838	D(G(z)): 0.0364 / 0.0167
[757/1000][0/4]	Loss_D: 0.0558	Loss_G: 4.2025	D(x): 0.9694	D(G(z)): 0.0237 / 0.0230
[758/1000][0/4]	Loss_D: 0.0603	Loss_G: 4.3444	D(x): 0.9779	D(G(z)): 0.0364 / 0.0211
[759/1000][0/4]	Loss_D: 0.0499	Loss_G: 4.2830	D(x): 0.9741	D(G(z)): 0.0229 / 0.0207
[760/1000][0/4]	Loss_D: 0.0498	Loss_G: 4.2661	D(x): 0.9726	D(G(z)): 0.0214 / 0.0207
[761/1000][0/4]	Loss_D: 0.0593	Loss_G: 4.6487	D(x): 0.9813	D(G(z)): 0.0389 / 0.0134
[762/1000][0/4]	Loss_D: 0.0610	Loss_G: 4.0943	D(x): 0.9594	D(G(z)): 0.0185 / 0.0293
[763/1000][0/4]	Loss_D: 0.0502	Loss_G: 4.2524	D(x): 0.9688	D(G(z)): 0.0179 / 0.0224
[764/1000][0/4]	Loss_D: 0.0493	Loss_G: 4.4362	D(x): 0.9788	D(G(z)): 0.0270 / 0.0188
[765/1000][0/4]	Loss_D: 0.0700	Loss_G: 5.0589	D(x): 0.9883	D(G(z)): 0.0551 / 0.0099
[766/1000][0/4]	Loss_D: 0.0756	Loss_G: 3.3722	D(x): 0.9440	D(G(z)): 0.0171 / 0.0544
[767/1000][0/4]	Loss_D: 0.0530	Loss_G: 4.3691	D(x): 0.9699	D(G(z)): 0.0217 / 0.0193
[768/1000][0/4]	Loss_D: 0.0561	Loss_G: 4.5080	D(x): 0.9851	D(G(z)): 0.0392 / 0.0165
[769/1000][0/4]	Loss_D: 0.0460	Loss_G: 4.5107	D(x): 0.9779	D(G(z)): 0.0229 / 0.0166
[770/1000][0/4]	Loss_D: 0.0559	Loss_G: 4.4255	D(x): 0.9793	D(G(z)): 0.0337 / 0.0178
[771/1000][0/4]	Loss_D: 0.0706	Loss_G: 4.9906	D(x): 0.9869	D(G(z)): 0.0542 / 0.0103
[772/1000][0/4]	Loss_D: 0.0480	Loss_G: 4.4295	D(x): 0.9841	D(G(z)): 0.0310 / 0.0172
[773/1000][0/4]	Loss_D: 0.0457	Loss_G: 4.7928	D(x): 0.9847	D(G(z)): 0.0290 / 0.0134
[774/1000][0/4]	Loss_D: 0.0874	Loss_G: 2.6893	D(x): 0.9278	D(G(z)): 0.0108 / 0.1143
[775/1000][0/4]	Loss_D: 0.9293	Loss_G: 1.0513	D(x): 0.5031	D(G(z)): 0.0019 / 0.4578
[776/1000][0/4]	Loss_D: 1.3885	Loss_G: 1.2579	D(x): 0.4921	D(G(z)): 0.2989 / 0.3603
[777/1000][0/4]	Loss_D: 0.9769	Loss_G: 1.3482	D(x): 0.5954	D(G(z)): 0.2770 / 0.3145
[778/1000][0/4]	Loss_D: 0.8003	Loss_G: 1.5649	D(x): 0.5700	D(G(z)): 0.1081 / 0.2746
[779/1000][0/4]	Loss_D: 0.4614	Loss_G: 3.8251	D(x): 0.8155	D(G(z)): 0.1748 / 0.0552
[780/1000][0/4]	Loss_D: 0.2575	Loss_G: 4.7008	D(x): 0.9423	D(G(z)): 0.1610 / 0.0213
[781/1000][0/4]	Loss_D: 0.1489	Loss_G: 3.4668	D(x): 0.9211	D(G(z)): 0.0567 / 0.0528
[782/1000][0/4]	Loss_D: 0.3246	Loss_G: 3.7302	D(x): 0.7666	D(G(z)): 0.0124 / 0.0578
[783/1000][0/4]	Loss_D: 0.1289	Loss_G: 3.8999	D(x): 0.9315	D(G(z)): 0.0493 / 0.0337
[784/1000][0/4]	Loss_D: 0.1152	Loss_G: 4.1635	D(x): 0.9547	D(G(z)): 0.0631 / 0.0239
[785/1000][0/4]	Loss_D: 0.1276	Loss_G: 4.8253	D(x): 0.9793	D(G(z)): 0.0944 / 0.0139
[786/1000][0/4]	Loss_D: 0.1245	Loss_G: 5.0965	D(x): 0.9865	D(G(z)): 0.0991 / 0.0101
[787/1000][0/4]	Loss_D: 0.0878	Loss_G: 4.5994	D(x): 0.9804	D(G(z)): 0.0634 / 0.0155
[788/1000][0/4]	Loss_D: 0.0671	Loss_G: 4.5216	D(x): 0.9814	D(G(z)): 0.0462 / 0.0178
[789/1000][0/4]	Loss_D: 0.0789	Loss_G: 4.4006	D(x): 0.9670	D(G(z)): 0.0424 / 0.0193
[790/1000][0/4]	Loss_D: 0.0745	Loss_G: 4.1806	D(x): 0.9601	D(G(z)): 0.0318 / 0.0239
[791/1000][0/4]	Loss_D: 0.0783	Loss_G: 4.2938	D(x): 0.9691	D(G(z)): 0.0443 / 0.0214
[792/1000][0/4]	Loss_D: 0.0750	Loss_G: 4.2158	D(x): 0.9702	D(G(z)): 0.0424 / 0.0229
[793/1000][0/4]	Loss_D: 0.0798	Loss_G: 4.5048	D(x): 0.9848	D(G(z)): 0.0607 / 0.0163
[794/1000][0/4]	Loss_D: 0.0630	Loss_G: 4.3989	D(x): 0.9768	D(G(z)): 0.0380 / 0.0189
[795/1000][0/4]	Loss_D: 0.0593	Loss_G: 4.3091	D(x): 0.9706	D(G(z)): 0.0285 / 0.0200
[796/1000][0/4]	Loss_D: 0.0659	Loss_G: 4.1016	D(x): 0.9711	D(G(z)): 0.0350 / 0.0260
[797/1000][0/4]	Loss_D: 0.0671	Loss_G: 4.0662	D(x): 0.9621	D(G(z)): 0.0272 / 0.0262
[798/1000][0/4]	Loss_D: 0.0583	Loss_G: 4.2677	D(x): 0.9691	D(G(z)): 0.0260 / 0.0218
[799/1000][0/4]	Loss_D: 0.0606	Loss_G: 4.1798	D(x): 0.9703	D(G(z)): 0.0292 / 0.0236
[800/1000][0/4]	Loss_D: 0.0972	Loss_G: 5.0678	D(x): 0.9916	D(G(z)): 0.0818 / 0.0095
[801/1000][0/4]	Loss_D: 0.0687	Loss_G: 4.8033	D(x): 0.9820	D(G(z)): 0.0475 / 0.0132
[802/1000][0/4]	Loss_D: 0.0621	Loss_G: 4.4066	D(x): 0.9745	D(G(z)): 0.0346 / 0.0197
[803/1000][0/4]	Loss_D: 0.0558	Loss_G: 4.5317	D(x): 0.9840	D(G(z)): 0.0381 / 0.0162
[804/1000][0/4]	Loss_D: 0.0500	Loss_G: 4.2387	D(x): 0.9701	D(G(z)): 0.0191 / 0.0205
[805/1000][0/4]	Loss_D: 0.0535	Loss_G: 4.5803	D(x): 0.9778	D(G(z)): 0.0300 / 0.0153
[806/1000][0/4]	Loss_D: 0.0497	Loss_G: 4.6803	D(x): 0.9833	D(G(z)): 0.0318 / 0.0143
[807/1000][0/4]	Loss_D: 0.0548	Loss_G: 4.2809	D(x): 0.9741	D(G(z)): 0.0275 / 0.0212
[808/1000][0/4]	Loss_D: 0.0527	Loss_G: 4.3027	D(x): 0.9742	D(G(z)): 0.0256 / 0.0216
[809/1000][0/4]	Loss_D: 0.0478	Loss_G: 4.3000	D(x): 0.9761	D(G(z)): 0.0229 / 0.0207
[810/1000][0/4]	Loss_D: 0.0439	Loss_G: 4.4365	D(x): 0.9757	D(G(z)): 0.0188 / 0.0174
[811/1000][0/4]	Loss_D: 0.0541	Loss_G: 4.4822	D(x): 0.9831	D(G(z)): 0.0356 / 0.0173
[812/1000][0/4]	Loss_D: 0.1029	Loss_G: 5.4040	D(x): 0.9935	D(G(z)): 0.0883 / 0.0063
[813/1000][0/4]	Loss_D: 0.0392	Loss_G: 4.7583	D(x): 0.9768	D(G(z)): 0.0154 / 0.0138
[814/1000][0/4]	Loss_D: 0.0460	Loss_G: 4.5821	D(x): 0.9685	D(G(z)): 0.0133 / 0.0158
[815/1000][0/4]	Loss_D: 0.0477	Loss_G: 4.3547	D(x): 0.9715	D(G(z)): 0.0183 / 0.0196
[816/1000][0/4]	Loss_D: 0.0471	Loss_G: 4.2439	D(x): 0.9763	D(G(z)): 0.0225 / 0.0220
[817/1000][0/4]	Loss_D: 0.1107	Loss_G: 5.6263	D(x): 0.9919	D(G(z)): 0.0938 / 0.0053
[818/1000][0/4]	Loss_D: 0.0580	Loss_G: 4.2446	D(x): 0.9573	D(G(z)): 0.0136 / 0.0235
[819/1000][0/4]	Loss_D: 0.0403	Loss_G: 4.5389	D(x): 0.9744	D(G(z)): 0.0139 / 0.0172
[820/1000][0/4]	Loss_D: 0.0411	Loss_G: 4.5274	D(x): 0.9851	D(G(z)): 0.0253 / 0.0162
[821/1000][0/4]	Loss_D: 0.0547	Loss_G: 4.8726	D(x): 0.9898	D(G(z)): 0.0424 / 0.0115
[822/1000][0/4]	Loss_D: 0.0476	Loss_G: 4.6245	D(x): 0.9827	D(G(z)): 0.0291 / 0.0152
[823/1000][0/4]	Loss_D: 0.0338	Loss_G: 4.8997	D(x): 0.9848	D(G(z)): 0.0180 / 0.0113
[824/1000][0/4]	Loss_D: 0.0356	Loss_G: 4.6485	D(x): 0.9789	D(G(z)): 0.0141 / 0.0138
[825/1000][0/4]	Loss_D: 0.0447	Loss_G: 4.2133	D(x): 0.9720	D(G(z)): 0.0159 / 0.0225
[826/1000][0/4]	Loss_D: 0.0473	Loss_G: 4.5480	D(x): 0.9841	D(G(z)): 0.0301 / 0.0161
[827/1000][0/4]	Loss_D: 0.0429	Loss_G: 4.7629	D(x): 0.9836	D(G(z)): 0.0255 / 0.0130
[828/1000][0/4]	Loss_D: 0.0432	Loss_G: 4.5876	D(x): 0.9862	D(G(z)): 0.0284 / 0.0152
[829/1000][0/4]	Loss_D: 0.0469	Loss_G: 4.8200	D(x): 0.9904	D(G(z)): 0.0358 / 0.0128
[830/1000][0/4]	Loss_D: 0.0394	Loss_G: 4.8608	D(x): 0.9814	D(G(z)): 0.0202 / 0.0129
[831/1000][0/4]	Loss_D: 0.0373	Loss_G: 4.6837	D(x): 0.9838	D(G(z)): 0.0204 / 0.0140
[832/1000][0/4]	Loss_D: 0.0419	Loss_G: 4.4136	D(x): 0.9704	D(G(z)): 0.0116 / 0.0181
[833/1000][0/4]	Loss_D: 0.0587	Loss_G: 5.1547	D(x): 0.9906	D(G(z)): 0.0467 / 0.0089
[834/1000][0/4]	Loss_D: 0.0372	Loss_G: 4.8799	D(x): 0.9847	D(G(z)): 0.0212 / 0.0129
[835/1000][0/4]	Loss_D: 0.0384	Loss_G: 4.6227	D(x): 0.9834	D(G(z)): 0.0211 / 0.0149
[836/1000][0/4]	Loss_D: 0.0374	Loss_G: 4.6908	D(x): 0.9855	D(G(z)): 0.0222 / 0.0139
[837/1000][0/4]	Loss_D: 0.0428	Loss_G: 5.2581	D(x): 0.9919	D(G(z)): 0.0332 / 0.0082
[838/1000][0/4]	Loss_D: 0.3954	Loss_G: 11.3641	D(x): 0.9980	D(G(z)): 0.2924 / 0.0000
[839/1000][0/4]	Loss_D: 6.3494	Loss_G: 0.0053	D(x): 0.0669	D(G(z)): 0.0513 / 0.9953
[840/1000][0/4]	Loss_D: 1.4546	Loss_G: 5.3974	D(x): 0.8054	D(G(z)): 0.5703 / 0.0290
[841/1000][0/4]	Loss_D: 1.0937	Loss_G: 2.6828	D(x): 0.8918	D(G(z)): 0.5185 / 0.1285
[842/1000][0/4]	Loss_D: 1.0345	Loss_G: 2.1524	D(x): 0.5675	D(G(z)): 0.2602 / 0.2152
[843/1000][0/4]	Loss_D: 0.6684	Loss_G: 2.9441	D(x): 0.6237	D(G(z)): 0.0758 / 0.1269
[844/1000][0/4]	Loss_D: 0.8563	Loss_G: 2.1314	D(x): 0.5359	D(G(z)): 0.0356 / 0.2115
[845/1000][0/4]	Loss_D: 0.3939	Loss_G: 4.4163	D(x): 0.8434	D(G(z)): 0.1496 / 0.0299
[846/1000][0/4]	Loss_D: 0.2981	Loss_G: 4.7430	D(x): 0.9255	D(G(z)): 0.1725 / 0.0161
[847/1000][0/4]	Loss_D: 0.4694	Loss_G: 7.4604	D(x): 0.9676	D(G(z)): 0.3009 / 0.0017
[848/1000][0/4]	Loss_D: 0.1504	Loss_G: 3.7930	D(x): 0.9122	D(G(z)): 0.0442 / 0.0443
[849/1000][0/4]	Loss_D: 0.1783	Loss_G: 4.4757	D(x): 0.9365	D(G(z)): 0.0963 / 0.0226
[850/1000][0/4]	Loss_D: 0.1656	Loss_G: 4.4024	D(x): 0.9298	D(G(z)): 0.0810 / 0.0218
[851/1000][0/4]	Loss_D: 0.1606	Loss_G: 4.1269	D(x): 0.9133	D(G(z)): 0.0606 / 0.0300
[852/1000][0/4]	Loss_D: 0.1407	Loss_G: 5.1010	D(x): 0.9598	D(G(z)): 0.0885 / 0.0108
[853/1000][0/4]	Loss_D: 0.1540	Loss_G: 4.9648	D(x): 0.9678	D(G(z)): 0.1076 / 0.0112
[854/1000][0/4]	Loss_D: 0.1100	Loss_G: 4.2714	D(x): 0.9371	D(G(z)): 0.0401 / 0.0229
[855/1000][0/4]	Loss_D: 0.1299	Loss_G: 4.6812	D(x): 0.9702	D(G(z)): 0.0887 / 0.0149
[856/1000][0/4]	Loss_D: 0.1044	Loss_G: 4.2499	D(x): 0.9412	D(G(z)): 0.0406 / 0.0254
[857/1000][0/4]	Loss_D: 0.1163	Loss_G: 4.4680	D(x): 0.9689	D(G(z)): 0.0776 / 0.0171
[858/1000][0/4]	Loss_D: 0.1035	Loss_G: 3.9644	D(x): 0.9376	D(G(z)): 0.0360 / 0.0306
[859/1000][0/4]	Loss_D: 0.1016	Loss_G: 4.2392	D(x): 0.9723	D(G(z)): 0.0681 / 0.0234
[860/1000][0/4]	Loss_D: 0.0881	Loss_G: 4.2019	D(x): 0.9632	D(G(z)): 0.0478 / 0.0234
[861/1000][0/4]	Loss_D: 0.0787	Loss_G: 4.1246	D(x): 0.9550	D(G(z)): 0.0312 / 0.0250
[862/1000][0/4]	Loss_D: 0.0888	Loss_G: 4.2187	D(x): 0.9549	D(G(z)): 0.0402 / 0.0233
[863/1000][0/4]	Loss_D: 0.0873	Loss_G: 4.8563	D(x): 0.9836	D(G(z)): 0.0662 / 0.0114
[864/1000][0/4]	Loss_D: 0.0862	Loss_G: 4.1231	D(x): 0.9414	D(G(z)): 0.0244 / 0.0279
[865/1000][0/4]	Loss_D: 0.1523	Loss_G: 5.6772	D(x): 0.9902	D(G(z)): 0.1253 / 0.0054
[866/1000][0/4]	Loss_D: 0.0706	Loss_G: 4.4308	D(x): 0.9617	D(G(z)): 0.0298 / 0.0186
[867/1000][0/4]	Loss_D: 0.0676	Loss_G: 4.6021	D(x): 0.9757	D(G(z)): 0.0409 / 0.0160
[868/1000][0/4]	Loss_D: 0.0592	Loss_G: 4.3789	D(x): 0.9673	D(G(z)): 0.0250 / 0.0196
[869/1000][0/4]	Loss_D: 0.0669	Loss_G: 4.2306	D(x): 0.9767	D(G(z)): 0.0413 / 0.0207
[870/1000][0/4]	Loss_D: 0.0674	Loss_G: 4.5789	D(x): 0.9637	D(G(z)): 0.0287 / 0.0168
[871/1000][0/4]	Loss_D: 0.0667	Loss_G: 4.2384	D(x): 0.9636	D(G(z)): 0.0283 / 0.0220
[872/1000][0/4]	Loss_D: 0.0671	Loss_G: 4.0869	D(x): 0.9688	D(G(z)): 0.0341 / 0.0242
[873/1000][0/4]	Loss_D: 0.1010	Loss_G: 5.2675	D(x): 0.9888	D(G(z)): 0.0827 / 0.0080
[874/1000][0/4]	Loss_D: 0.0499	Loss_G: 4.5680	D(x): 0.9761	D(G(z)): 0.0250 / 0.0161
[875/1000][0/4]	Loss_D: 0.0643	Loss_G: 4.4266	D(x): 0.9636	D(G(z)): 0.0260 / 0.0219
[876/1000][0/4]	Loss_D: 0.0580	Loss_G: 3.9261	D(x): 0.9613	D(G(z)): 0.0179 / 0.0304
[877/1000][0/4]	Loss_D: 0.0724	Loss_G: 3.9908	D(x): 0.9687	D(G(z)): 0.0388 / 0.0292
[878/1000][0/4]	Loss_D: 0.0665	Loss_G: 4.4184	D(x): 0.9856	D(G(z)): 0.0493 / 0.0175
[879/1000][0/4]	Loss_D: 0.0602	Loss_G: 4.4024	D(x): 0.9825	D(G(z)): 0.0406 / 0.0183
[880/1000][0/4]	Loss_D: 0.0544	Loss_G: 4.7884	D(x): 0.9807	D(G(z)): 0.0336 / 0.0131
[881/1000][0/4]	Loss_D: 0.0591	Loss_G: 4.5284	D(x): 0.9781	D(G(z)): 0.0353 / 0.0170
[882/1000][0/4]	Loss_D: 0.0516	Loss_G: 4.6518	D(x): 0.9791	D(G(z)): 0.0293 / 0.0140
[883/1000][0/4]	Loss_D: 0.0513	Loss_G: 4.6471	D(x): 0.9839	D(G(z)): 0.0338 / 0.0148
[884/1000][0/4]	Loss_D: 0.0481	Loss_G: 4.3163	D(x): 0.9763	D(G(z)): 0.0235 / 0.0201
[885/1000][0/4]	Loss_D: 0.0495	Loss_G: 4.2471	D(x): 0.9702	D(G(z)): 0.0187 / 0.0213
[886/1000][0/4]	Loss_D: 0.0629	Loss_G: 3.9180	D(x): 0.9637	D(G(z)): 0.0249 / 0.0324
[887/1000][0/4]	Loss_D: 0.0483	Loss_G: 4.3620	D(x): 0.9831	D(G(z)): 0.0303 / 0.0188
[888/1000][0/4]	Loss_D: 0.0569	Loss_G: 4.2618	D(x): 0.9761	D(G(z)): 0.0313 / 0.0214
[889/1000][0/4]	Loss_D: 0.0580	Loss_G: 4.8957	D(x): 0.9916	D(G(z)): 0.0472 / 0.0115
[890/1000][0/4]	Loss_D: 0.0497	Loss_G: 5.0136	D(x): 0.9884	D(G(z)): 0.0368 / 0.0100
[891/1000][0/4]	Loss_D: 0.0475	Loss_G: 4.5843	D(x): 0.9799	D(G(z)): 0.0262 / 0.0160
[892/1000][0/4]	Loss_D: 0.0481	Loss_G: 4.5147	D(x): 0.9757	D(G(z)): 0.0228 / 0.0167
[893/1000][0/4]	Loss_D: 0.0491	Loss_G: 4.6701	D(x): 0.9843	D(G(z)): 0.0322 / 0.0146
[894/1000][0/4]	Loss_D: 0.0400	Loss_G: 4.6071	D(x): 0.9820	D(G(z)): 0.0212 / 0.0150
[895/1000][0/4]	Loss_D: 0.0415	Loss_G: 4.4928	D(x): 0.9729	D(G(z)): 0.0137 / 0.0186
[896/1000][0/4]	Loss_D: 0.0420	Loss_G: 4.5318	D(x): 0.9858	D(G(z)): 0.0269 / 0.0160
[897/1000][0/4]	Loss_D: 0.0395	Loss_G: 4.5907	D(x): 0.9773	D(G(z)): 0.0163 / 0.0153
[898/1000][0/4]	Loss_D: 0.0460	Loss_G: 4.5725	D(x): 0.9885	D(G(z)): 0.0332 / 0.0163
[899/1000][0/4]	Loss_D: 0.0428	Loss_G: 4.6568	D(x): 0.9832	D(G(z)): 0.0250 / 0.0150
[900/1000][0/4]	Loss_D: 0.0428	Loss_G: 4.7111	D(x): 0.9828	D(G(z)): 0.0247 / 0.0138
[901/1000][0/4]	Loss_D: 0.0448	Loss_G: 4.3746	D(x): 0.9733	D(G(z)): 0.0171 / 0.0204
[902/1000][0/4]	Loss_D: 0.0430	Loss_G: 4.5524	D(x): 0.9800	D(G(z)): 0.0218 / 0.0169
[903/1000][0/4]	Loss_D: 0.0333	Loss_G: 4.5556	D(x): 0.9818	D(G(z)): 0.0146 / 0.0156
[904/1000][0/4]	Loss_D: 0.0639	Loss_G: 3.5000	D(x): 0.9468	D(G(z)): 0.0087 / 0.0496
[905/1000][0/4]	Loss_D: 0.0422	Loss_G: 4.7212	D(x): 0.9836	D(G(z)): 0.0249 / 0.0137
[906/1000][0/4]	Loss_D: 0.0371	Loss_G: 4.7173	D(x): 0.9846	D(G(z)): 0.0210 / 0.0143
[907/1000][0/4]	Loss_D: 0.0387	Loss_G: 4.9486	D(x): 0.9860	D(G(z)): 0.0238 / 0.0115
[908/1000][0/4]	Loss_D: 0.0383	Loss_G: 4.3886	D(x): 0.9763	D(G(z)): 0.0134 / 0.0198
[909/1000][0/4]	Loss_D: 0.0687	Loss_G: 5.3442	D(x): 0.9939	D(G(z)): 0.0590 / 0.0071
[910/1000][0/4]	Loss_D: 0.0416	Loss_G: 4.6560	D(x): 0.9684	D(G(z)): 0.0092 / 0.0148
[911/1000][0/4]	Loss_D: 0.0367	Loss_G: 4.7823	D(x): 0.9871	D(G(z)): 0.0230 / 0.0136
[912/1000][0/4]	Loss_D: 0.0321	Loss_G: 4.6873	D(x): 0.9842	D(G(z)): 0.0159 / 0.0141
[913/1000][0/4]	Loss_D: 0.0411	Loss_G: 4.4374	D(x): 0.9739	D(G(z)): 0.0142 / 0.0205
[914/1000][0/4]	Loss_D: 0.0378	Loss_G: 4.3979	D(x): 0.9815	D(G(z)): 0.0187 / 0.0186
[915/1000][0/4]	Loss_D: 0.0342	Loss_G: 4.6519	D(x): 0.9854	D(G(z)): 0.0191 / 0.0143
[916/1000][0/4]	Loss_D: 0.0547	Loss_G: 3.7199	D(x): 0.9603	D(G(z)): 0.0130 / 0.0396
[917/1000][0/4]	Loss_D: 0.0351	Loss_G: 4.5978	D(x): 0.9837	D(G(z)): 0.0183 / 0.0153
[918/1000][0/4]	Loss_D: 0.0326	Loss_G: 4.6999	D(x): 0.9818	D(G(z)): 0.0137 / 0.0151
[919/1000][0/4]	Loss_D: 0.0452	Loss_G: 4.2423	D(x): 0.9669	D(G(z)): 0.0108 / 0.0248
[920/1000][0/4]	Loss_D: 0.0384	Loss_G: 4.8234	D(x): 0.9900	D(G(z)): 0.0271 / 0.0128
[921/1000][0/4]	Loss_D: 0.0343	Loss_G: 4.7377	D(x): 0.9818	D(G(z)): 0.0156 / 0.0144
[922/1000][0/4]	Loss_D: 0.0433	Loss_G: 5.1786	D(x): 0.9908	D(G(z)): 0.0328 / 0.0093
[923/1000][0/4]	Loss_D: 0.0335	Loss_G: 4.7966	D(x): 0.9820	D(G(z)): 0.0150 / 0.0128
[924/1000][0/4]	Loss_D: 0.0444	Loss_G: 5.0181	D(x): 0.9902	D(G(z)): 0.0333 / 0.0099
[925/1000][0/4]	Loss_D: 0.0425	Loss_G: 4.1970	D(x): 0.9661	D(G(z)): 0.0078 / 0.0238
[926/1000][0/4]	Loss_D: 0.0334	Loss_G: 4.5347	D(x): 0.9805	D(G(z)): 0.0135 / 0.0176
[927/1000][0/4]	Loss_D: 0.0322	Loss_G: 4.6405	D(x): 0.9844	D(G(z)): 0.0162 / 0.0150
[928/1000][0/4]	Loss_D: 0.0370	Loss_G: 4.4046	D(x): 0.9729	D(G(z)): 0.0094 / 0.0204
[929/1000][0/4]	Loss_D: 0.0321	Loss_G: 4.6786	D(x): 0.9829	D(G(z)): 0.0146 / 0.0157
[930/1000][0/4]	Loss_D: 0.0478	Loss_G: 3.6697	D(x): 0.9610	D(G(z)): 0.0078 / 0.0424
[931/1000][0/4]	Loss_D: 0.0378	Loss_G: 5.0245	D(x): 0.9868	D(G(z)): 0.0238 / 0.0102
[932/1000][0/4]	Loss_D: 0.0331	Loss_G: 5.0608	D(x): 0.9918	D(G(z)): 0.0242 / 0.0096
[933/1000][0/4]	Loss_D: 0.0354	Loss_G: 5.1311	D(x): 0.9885	D(G(z)): 0.0232 / 0.0092
[934/1000][0/4]	Loss_D: 0.0344	Loss_G: 4.4802	D(x): 0.9789	D(G(z)): 0.0128 / 0.0185
[935/1000][0/4]	Loss_D: 0.0282	Loss_G: 4.7491	D(x): 0.9881	D(G(z)): 0.0160 / 0.0131
[936/1000][0/4]	Loss_D: 0.0432	Loss_G: 5.0048	D(x): 0.9918	D(G(z)): 0.0339 / 0.0098
[937/1000][0/4]	Loss_D: 0.0290	Loss_G: 5.1220	D(x): 0.9877	D(G(z)): 0.0160 / 0.0092
[938/1000][0/4]	Loss_D: 0.0287	Loss_G: 5.0568	D(x): 0.9901	D(G(z)): 0.0183 / 0.0108
[939/1000][0/4]	Loss_D: 0.0343	Loss_G: 5.0628	D(x): 0.9912	D(G(z)): 0.0245 / 0.0100
[940/1000][0/4]	Loss_D: 0.0323	Loss_G: 4.7683	D(x): 0.9798	D(G(z)): 0.0117 / 0.0136
[941/1000][0/4]	Loss_D: 0.0355	Loss_G: 4.6448	D(x): 0.9866	D(G(z)): 0.0215 / 0.0149
[942/1000][0/4]	Loss_D: 0.0371	Loss_G: 4.7369	D(x): 0.9775	D(G(z)): 0.0139 / 0.0144
[943/1000][0/4]	Loss_D: 0.0309	Loss_G: 4.4498	D(x): 0.9810	D(G(z)): 0.0115 / 0.0186
[944/1000][0/4]	Loss_D: 0.0318	Loss_G: 4.6130	D(x): 0.9846	D(G(z)): 0.0159 / 0.0157
[945/1000][0/4]	Loss_D: 0.1155	Loss_G: 7.7043	D(x): 0.9972	D(G(z)): 0.1014 / 0.0008
[946/1000][0/4]	Loss_D: 2.1957	Loss_G: 5.1388	D(x): 0.7630	D(G(z)): 0.6383 / 0.0249
[947/1000][0/4]	Loss_D: 1.4530	Loss_G: 1.5914	D(x): 0.6427	D(G(z)): 0.4745 / 0.2736
[948/1000][0/4]	Loss_D: 1.2311	Loss_G: 1.5239	D(x): 0.6219	D(G(z)): 0.4624 / 0.2621
[949/1000][0/4]	Loss_D: 1.6407	Loss_G: 2.5813	D(x): 0.8751	D(G(z)): 0.7153 / 0.1161
[950/1000][0/4]	Loss_D: 1.0477	Loss_G: 1.1749	D(x): 0.5793	D(G(z)): 0.3330 / 0.3412
[951/1000][0/4]	Loss_D: 1.1078	Loss_G: 0.8461	D(x): 0.5098	D(G(z)): 0.2286 / 0.4726
[952/1000][0/4]	Loss_D: 1.1378	Loss_G: 1.5185	D(x): 0.4794	D(G(z)): 0.1723 / 0.2804
[953/1000][0/4]	Loss_D: 1.0230	Loss_G: 3.0362	D(x): 0.7688	D(G(z)): 0.4243 / 0.1064
[954/1000][0/4]	Loss_D: 1.1897	Loss_G: 3.9047	D(x): 0.8456	D(G(z)): 0.5448 / 0.0468
[955/1000][0/4]	Loss_D: 0.7074	Loss_G: 2.9901	D(x): 0.8173	D(G(z)): 0.3363 / 0.0902
[956/1000][0/4]	Loss_D: 0.8158	Loss_G: 3.0035	D(x): 0.7026	D(G(z)): 0.2848 / 0.1059
[957/1000][0/4]	Loss_D: 1.8746	Loss_G: 1.8732	D(x): 0.2787	D(G(z)): 0.0202 / 0.3085
[958/1000][0/4]	Loss_D: 0.6756	Loss_G: 3.7724	D(x): 0.8819	D(G(z)): 0.3661 / 0.0549
[959/1000][0/4]	Loss_D: 0.7537	Loss_G: 5.1062	D(x): 0.8797	D(G(z)): 0.3648 / 0.0225
[960/1000][0/4]	Loss_D: 0.8536	Loss_G: 5.5302	D(x): 0.9499	D(G(z)): 0.4480 / 0.0155
[961/1000][0/4]	Loss_D: 0.6601	Loss_G: 2.4023	D(x): 0.6367	D(G(z)): 0.0502 / 0.1800
[962/1000][0/4]	Loss_D: 0.3687	Loss_G: 8.4081	D(x): 0.9554	D(G(z)): 0.2295 / 0.0031
[963/1000][0/4]	Loss_D: 1.6035	Loss_G: 7.4175	D(x): 0.9884	D(G(z)): 0.6581 / 0.0033
[964/1000][0/4]	Loss_D: 0.4976	Loss_G: 4.0817	D(x): 0.7143	D(G(z)): 0.0473 / 0.0426
[965/1000][0/4]	Loss_D: 0.2407	Loss_G: 5.0553	D(x): 0.9239	D(G(z)): 0.1238 / 0.0158
[966/1000][0/4]	Loss_D: 0.2482	Loss_G: 5.1884	D(x): 0.9256	D(G(z)): 0.1364 / 0.0123
[967/1000][0/4]	Loss_D: 0.4676	Loss_G: 9.3768	D(x): 0.9795	D(G(z)): 0.3031 / 0.0008
[968/1000][0/4]	Loss_D: 0.1961	Loss_G: 4.3664	D(x): 0.8853	D(G(z)): 0.0574 / 0.0301
[969/1000][0/4]	Loss_D: 0.1671	Loss_G: 4.5768	D(x): 0.9161	D(G(z)): 0.0648 / 0.0235
[970/1000][0/4]	Loss_D: 0.1615	Loss_G: 5.8823	D(x): 0.9658	D(G(z)): 0.1087 / 0.0072
[971/1000][0/4]	Loss_D: 0.2031	Loss_G: 3.9065	D(x): 0.8515	D(G(z)): 0.0212 / 0.0379
[972/1000][0/4]	Loss_D: 0.0942	Loss_G: 4.5115	D(x): 0.9555	D(G(z)): 0.0444 / 0.0213
[973/1000][0/4]	Loss_D: 0.0955	Loss_G: 4.9339	D(x): 0.9679	D(G(z)): 0.0571 / 0.0138
[974/1000][0/4]	Loss_D: 0.1080	Loss_G: 4.7905	D(x): 0.9625	D(G(z)): 0.0634 / 0.0147
[975/1000][0/4]	Loss_D: 0.0897	Loss_G: 4.1751	D(x): 0.9437	D(G(z)): 0.0293 / 0.0251
[976/1000][0/4]	Loss_D: 0.1083	Loss_G: 4.5732	D(x): 0.9614	D(G(z)): 0.0621 / 0.0184
[977/1000][0/4]	Loss_D: 0.0938	Loss_G: 4.9430	D(x): 0.9813	D(G(z)): 0.0691 / 0.0120
[978/1000][0/4]	Loss_D: 0.0807	Loss_G: 4.6343	D(x): 0.9640	D(G(z)): 0.0397 / 0.0161
[979/1000][0/4]	Loss_D: 0.0656	Loss_G: 4.7958	D(x): 0.9694	D(G(z)): 0.0327 / 0.0139
[980/1000][0/4]	Loss_D: 0.0685	Loss_G: 4.7601	D(x): 0.9742	D(G(z)): 0.0401 / 0.0133
[981/1000][0/4]	Loss_D: 0.0827	Loss_G: 4.1900	D(x): 0.9521	D(G(z)): 0.0312 / 0.0240
[982/1000][0/4]	Loss_D: 0.0726	Loss_G: 4.2447	D(x): 0.9567	D(G(z)): 0.0269 / 0.0230
[983/1000][0/4]	Loss_D: 0.0669	Loss_G: 4.1617	D(x): 0.9629	D(G(z)): 0.0275 / 0.0237
[984/1000][0/4]	Loss_D: 0.0714	Loss_G: 4.7856	D(x): 0.9711	D(G(z)): 0.0397 / 0.0134
[985/1000][0/4]	Loss_D: 0.0494	Loss_G: 4.3924	D(x): 0.9688	D(G(z)): 0.0171 / 0.0194
[986/1000][0/4]	Loss_D: 0.1028	Loss_G: 5.3189	D(x): 0.9903	D(G(z)): 0.0847 / 0.0079
[987/1000][0/4]	Loss_D: 0.0778	Loss_G: 5.3133	D(x): 0.9834	D(G(z)): 0.0565 / 0.0093
[988/1000][0/4]	Loss_D: 0.0541	Loss_G: 4.7271	D(x): 0.9789	D(G(z)): 0.0314 / 0.0147
[989/1000][0/4]	Loss_D: 0.0773	Loss_G: 5.1547	D(x): 0.9910	D(G(z)): 0.0639 / 0.0091
[990/1000][0/4]	Loss_D: 0.0479	Loss_G: 4.8163	D(x): 0.9729	D(G(z)): 0.0195 / 0.0135
[991/1000][0/4]	Loss_D: 0.0577	Loss_G: 4.2146	D(x): 0.9669	D(G(z)): 0.0225 / 0.0232
[992/1000][0/4]	Loss_D: 0.0475	Loss_G: 4.6179	D(x): 0.9789	D(G(z)): 0.0252 / 0.0159
[993/1000][0/4]	Loss_D: 0.0748	Loss_G: 5.3377	D(x): 0.9897	D(G(z)): 0.0596 / 0.0080
[994/1000][0/4]	Loss_D: 0.0519	Loss_G: 4.6518	D(x): 0.9684	D(G(z)): 0.0188 / 0.0164
[995/1000][0/4]	Loss_D: 0.0544	Loss_G: 4.2822	D(x): 0.9689	D(G(z)): 0.0220 / 0.0240
[996/1000][0/4]	Loss_D: 0.0515	Loss_G: 4.6906	D(x): 0.9838	D(G(z)): 0.0339 / 0.0140
[997/1000][0/4]	Loss_D: 0.0679	Loss_G: 4.1732	D(x): 0.9435	D(G(z)): 0.0078 / 0.0293
[998/1000][0/4]	Loss_D: 0.0539	Loss_G: 4.1509	D(x): 0.9692	D(G(z)): 0.0211 / 0.0264
[999/1000][0/4]	Loss_D: 0.0405	Loss_G: 4.7926	D(x): 0.9784	D(G(z)): 0.0182 / 0.0128

plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()

fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)

HTML(ani.to_jshtml())

Once Loop Reflect

# Grab a batch of real images from the dataloader
real_batch = next(iter(dataloader))

# Plot the real images
plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.axis("off")
plt.title("Real Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))

# Plot the fake images from the last epoch
plt.subplot(1,2,2)
plt.axis("off")
plt.title("Fake Images")
plt.imshow(np.transpose(img_list[-1],(1,2,0)))
plt.show()